Scoring of tumor infiltration by lymphocytes

ABSTRACT

A method of providing a prognosis in a cancer patient comprising analysing a tumour image to calculate a metric of immune infiltration for the tumour, and a method of analysing a tumour image.

FIELD OF THE INVENTION

The present invention relates to tumour analysis, and to cancerprognosis. In particular, the present invention relates to methods ofanalysing tumours for determining a prognosis in cancer.

BACKGROUND

Cancer is a complex and dynamic disease, and many different ways ofanalysing and classifying tumours have been developed with the aims ofdetermining the degree of tumour progression or invasiveness and theprognosis for the patient, and informing treatment decisions.

Methods of analysing tumours include the assessment of cell morphologyin tumours (typically performed by pathologists), measurement of geneexpression in tumours (e.g. by microarray analysis), determination ofgene mutation status in tumour cells, and evaluating protein expressionwithin tumours (e.g. by immunohistochemical assessment of tumoursections). These methods of analysing tumours are important not only forpredicting clinical outcome, but also for informing decisions on patienttherapy.

More recently, it has become apparent that the immunological status oftumours can yield useful prognostic information. Accumulating evidencesupports the clinical significance of immune response in many cancertypes (Galon et al. 2006, Denkert et al. 2010, Loi et al.). Consistentstudies have reported associations between immune activity and diseaseoutcome as well as treatment response (Galon et al. 2006, Denkert et al.2010, Loi et al., Liu et al., Lee et al., DeNardo et al.).

Furthermore, increasing evidence from clinical trials supports thepotential of therapies that target immune activity in certain types ofcancer (Robert et al., Stagg et al.). This is perhaps best exemplifiedin late stage melanoma where recent clinical trials have shown anincreased survival advantage in patients receiving the monoclonalantibody ipilimumab, which targets the CTLA4 protein receptor that isexpressed on the surface of T cells (Robert et al.). This has led to thedevelopment of more standardised methods of characterising tumour immuneinfiltrate in cancers such as the “immunescore” that aims to quantifythe in situ immune infiltrate in addition to standardised clinicalparameters to aid prognostication and patient selection forimmunotherapy in colorectal cancers (Galon et al. 2014).

However, to facilitate the standardisation and reproducibility ofscoring immune infiltration, objective approaches are urgently needed(Galon et al. 2014). Furthermore, such approaches need to account forthe complexity of immune infiltration into tumours. Abundance, spatialheterogeneity and type of immune cells are the key parameters of immuneinfiltration (Galon et al. 2014, Fridman et al.). For example, thespatial locations of immune cells have been shown to be useful inpredicting the prognosis of colorectal cancer (Galon et al. 2006).Indeed the pathological “immunescore” is based on the numeration of twolymphocyte populations (CD8+ and CD45RO+ cells), both in the core of thetumour and in the invasive margin that maximises the prognostic power(Galon et al. 2014).

Similarly, large-scale studies of breast cancer have demonstrated thatpathological assessment of tumour-infiltration lymphocytes based onHematoxylin & Eosin (H&E) stained core biopsies is a significantpredictor for response to neoadjuvant chemotherapy in 1,058 breastcancer samples (Denkert et al. 2010). Recently, a prospective studydemonstrated that in HER2-negative breast cancer stromal lymphocytes canbe an independent predictor of response to neoadjuvant chemotherapy(Issa-Nummer et al.). Thus, the spatial organisation of lymphocyticinfiltration in the context of nearby cancer cells is an importantclinicopathological feature of tumours.

In triple-negative breast cancer (TNBC) an active immune response hasbeen associated with favourable prognosis (Loi et al., Liu et al.,Denkert et al.). A large-scale immunohistochemistry study of 3,400breast cancer samples has showed that TNBC is the only subtype of breastcancer to demonstrate a significant link between CDS-positive immunecells and a good prognosis (Liu et al.). Assessment of lymphocyticinfiltration based on whole-tumour H&E sections has been associated withfavourable outcome in 256 patients after anthracycline-basedchemotherapy (Loi et al.). A recent prospective study showed that thepresence of tumour-infiltrating lymphocytes in residual tumours afterneoadjuvant chemotherapy is predictive of good prognosis in TNBC (Dieciet al.). Given the current lack of targeted molecular treatment and poorclinical outcome of TNBC, this may suggest new therapeutic opportunitiesfor this aggressive tumour type (Stagg et al.). For instance,accumulating data suggest that anthracyclines mediate their actionthrough activation of CD8+ T-cell responses, hence combination withcertain immunotherapies could be especially effective for TNBC (Stagg etal.).

However, despite these advances in understanding of the importance ofimmune infiltration in cancer, there is a lack of reproducibleapproaches to objectively assess immune infiltration based onpathological sections.

SUMMARY OF THE INVENTION

Lymphocytic infiltration in tumours is often associated with afavourable prognosis and predicts response to chemotherapy in manycancer types. However, it is not well understood because the high levelsof spatial and molecular heterogeneity within tumours make it difficultto analyse by traditional pathological assessment.

Identification of cell types by pathologists in the assessment of immuneinfiltration provides qualitative information on coarse ordinal scales.Such information is poorly suited to analysing large data collections,partly because the high amount human input required renders large scalestudies time-consuming and expensive, partly because the subjectivenature of the assessment causes an unacceptable degree of variability inthe information, and partly because the qualitative data generated donot lend themselves to statistical analysis.

The inventor has devised a robust and reproducible method forobjectively assessing immune infiltration in tumours. The method isperformed on a tumour image in which lymphocytes and cancer cells havebeen identified.

The method may be performed on images of hematoxylin & eosin (H&E)stained tumour sections. H&E stained sections, and images of H&E stainedsections, are often readily available as part of data sets collected forcancer study groups such as the METABRIC group (Curtis, 2012) and theCancer Genome Atlas (TCGA) group (TCGA, 2012), which makes the methodsof the present invention readily adaptable for use in analysing tumoursfrom a variety of cancer types. The method may comprise a step oftreating a tumour section with a stain, such as H&E, wherein thepresence of subcellular structures such as nuclei creates complexesbetween the stain and the subcellular structure.

An aspect of the present invention provides a method of measuring immuneinfiltration in a tumour. In particular, there is provided a method ofdetermining an objective measurement of immune infiltration in a tumour,referred to herein as the ITLR. The ITLR (Intra-Tumour Lymphocyte Ratio)is the ratio of intra-tumour lymphocytes to cancer cells in the tumourexpressed as a decimal fraction. For example a ratio of 11 intra-tumourlymphocytes to 1000 cancer cells corresponds to an ITLR of 0.011.

Accordingly, an aspect of the invention provides a method of measuringimmune infiltration in a tumour, the method comprising:

-   -   providing an image of the tumour in which lymphocytes and cancer        cells have been identified;    -   obtaining a lymphocyte-to-cancer measurement for each        lymphocyte;

classifying a subset of the lymphocytes as intra-tumour lymphocytesaccording to their lymphocyte-to-cancer ratio;

-   -   quantifying the intra-tumour lymphocytes and the cancer cells in        the tumour image;

calculating the intra-tumour lymphocyte ratio (ITLR) as the ratio ofintra-tumour lymphocytes to cancer cells, wherein the ITLR is ameasurement of immune infiltration in the tumour.

A further aspect of the present invention provides a method ofdetermining a cut-off value for ITLR for use in determining a prognosisin cancer, wherein an ITLR below the cut-off value indicates a poorprognosis. The method comprises determining the ITLR for a plurality oftumours, wherein each tumour is from a respective cancer patient in acohort of cancer patients, and selecting a cut-off value for the ITLRwherein patients with an ITLR lower than the cut-off value have a worseprognosis compared with patients with an ITLR equal to or higher thanthe cut-off value.

Accordingly, an aspect of the invention provides a method of determiningan ITLR cut-off value for a cancer type or subtype, for use in providinga prognosis in a cancer patient having that cancer type, the methodcomprising:

-   -   measuring immune infiltration in a tumour from each member of a        cohort of cancer patients having the cancer type or subtype        according to the methods described herein, thereby calculating        the ITLR for each tumour;    -   relating the ITLR for each tumour to the clinical outcome of        each cancer patient in the cohort of cancer patients; and    -   selecting a cut-off value for ITLR, wherein an ITLR equal to or        below the cut-off value is associated with a significantly worse        clinical outcome in the cohort of cancer patients than an ITLR        above the cut-off value

A further aspect of the present invention provides a method of providinga prognosis in cancer. In particular, there is provided a method ofusing ITLR as a prognostic biomarker for a cancer patient. The methodmay comprise measuring the ITLR of a tumour from a cancer patient andusing the ITLR to determine a prognosis for the patient. The method maycomprise determining the ITLR in a tumour from a cancer patient andusing the ITLR to determine a prognosis for the patient, wherein an ITLRbelow a predetermined cut-off value indicates a poor prognosis.

Accordingly, an aspect of the invention provides a method of providing aprognosis in a cancer patient, the method comprising:

-   -   measuring immune infiltration in a tumour from the cancer        patient according to the methods described herein, thereby        calculating the ITLR for the tumour,    -   wherein an ITLR below a predetermined ITLR cut-off value        indicates a poor prognosis.

The present invention further provides a method of treating cancer in apatient, the method comprising determining the ITLR in a tumour from thepatient or requesting a test providing the results of an analysis todetermine the ITLR in a tumour from the patient, and treating thepatient according to a therapeutic regime depending on whether the ITLRis equal to or below, or above, a predetermined-cut-off value.

SUMMARY OF THE FIGURES

FIG. 1. Intra-tumour heterogeneity of cancer cell and lymphocytedistributions.

A. 3D landscapes illustrating the spatial heterogeneity of cancer cellsand lymphocytes in an H&E breast whole-tumour section. The height of thehills in the 3D landscape represents the density of cells. B. Combinedanalysis of the spatial distribution of cancer and lymphocytes can leadto quantification of lymphocytic infiltration. Shown are a small H&Eimage and the corresponding 3D cancer density map, which facilitate themeasurement of spatial proximity to cancer for every single lymphocytein the image.

FIG. 2. Quantifying the intra-tumour heterogeneity of lymphocyticinfiltration.

A. Schematic depiction of the computational pipeline exemplified with asmall region of a breast cancer H&E section: H&E image; classified cellsusing automated image analysis; a map of cancer density based on imageanalysis result to quantify cancer-immune spatial relationships. B.Discovery of three categories of lymphocytes with unsupervisedclustering based on the spatial proximities of lymphocytes to cancer ina subset of TNBC samples. These data were then used to predict thecategories of all lymphocytes in all TNBC samples. C. Optimal number ofcluster K as suggested by BIC over 200 random sampling are 3 in 97% ofthe time and 5 (3%). BIC curves for the 200 sampling are showed on theleft, and boxplot showing means of clusters for K=3 solutions in 200sampling on the right. D. Illustration of the distance to the nearestcancer cell d_(min) and the distance to the centroid of convex hullregion formed by 10 nearby cancer cells d_(centroid) E. Boxplots to showthe differences among lymphocyte classes in terms of d_(min) andd_(centroid) (p-values by t-test). F. Scatter plot showing drain andd_(centroid) for 1,000 randomly selected lymphocytes, coloured based onthe three classes; dashed ellipses showing three clusters fitted tod_(min) and d_(centroid).

FIG. 3. A representative example illustrating three classes oflymphocytes in cancer density map of a tumour (middle section).

A. Density map of cancer and the spatial distribution of three classesof lymphocytes (spatial points coloured according to the classes). Blackcontour lines denote cut-off thresholds for the three classes oflymphocytes according to cancer density. B. Histogram showing the threetypes of lymphocytes in this sample. C. A higher resolution image of aregion in this sample; colour codes follow A.

FIG. 4. Association between ITLR and clinical parameters of TNBC.

A. Proportions of three classes of lymphocytes in 181 TNBCs. B. Triangleplot to show the lymphocyte composition for each tumour (each black dotrepresents a tumour; thin lines mark the 50% of corresponding axis). C.Boxplot to show correlation between pathological scores and ITLR;p-value from JT-test; n=patient number is each group; whiskers extend to1.5 interquantile range. D. Association between ITLR and tumour size,node status and TP53 mutations; whiskers extend to 1.5 interquantilerange. E. Distribution of ITLR in two cohorts with optimal cut-offsmarked as dashed red lines. F. Kaplan-Meier curves to illustrate thedisease-specific survival probabilities of patient groups in two TNBCcohorts stratified by ITLR using the cut-off selected in Cohort 1.Numbers in the legend show the number of patients in each group andnumbers in the bracket show the number of disease-specific deaths. G.Using Cohort 2 as the discovery cohort and Cohort 1 as the validationcohort yielded similar optimal cut-off.

FIG. 5. Comparison of ITLR with other immune signatures.

The optimal cut-off were selected in Cohort 1 and tested in Cohort 2 forA. image-based lymphocyte abundance (Lym); B. gene expression immunesignature by Calabro et al. (18); C. Ascierto et al. (19); D. IL8signature (20); E. CXCL13 expression. F. Comparing optimal cut-offsselected in two cohorts. Data were centred at 0 and scaled to havestandard deviation 1 and cut-offs were mapped to the centred, scaleddata. Signatures close to the diagonal line have similar cut-offs in twocohorts.

FIG. 6. ITLR-associated gene modules.

A. Kaplan-Meier curves to illustrate differences in disease-specificsurvival of patient groups of equal sizes stratified based on theexpression of key genes in three modules. B. Kaplan-Meier curves toillustrate differences in disease-specific survival of patient groupsstratified with CTLA4 expression by the lower 25, middle 50 and higher25 percentiles, ITLR, and CTLA4 and ITLR combined. Survival differencebetween CTLA low and high stratification within the ITLR high group isgiven as a p-value.

FIG. 7. Kaplan-Meier curves to illustrate the disease-specific survivalprobabilities of patient groups in in two TNBC cohorts stratified byATLR (Adjacent) and DTLR (Distal).

The signatures were dichotomised using a cut-off selected over a rangeof percentiles based on Cohort 1 (the left and middle columns) andtested in Cohort 2 (the right column). Dashed lines in the plots on theleft marks the significance threshold of p=0.05, and solid verticallines show the best cut-offs. For the Kaplan-Meier curves, the numbersin the legend show the number of patients in each group and numbers inthe bracket show the number of disease-specific deaths.

FIG. 8. Kaplan-Meier curves to illustrate the disease-specific survivalprobabilities of patient groups in in two TNBC cohorts stratified byATLR (Adjacent) and DTLR (Distal).

The signatures were dichotomised using a cut-off selected over a rangeof percentiles based on Cohort 2 (the left and right columns) and testedin Cohort 1 (the middle column).

FIG. 9. Kaplan-Meier curves to illustrate the disease-specific survivalprobabilities of patient groups in in two TNBC cohorts stratified bynine immune signatures.

The signatures were dichotomised using a cut-off selected over a rangeof percentiles based on Cohort 1 (the left and middle columns) andtested in Cohort 2 (the right column). Dashed lines in the plots on theleft marks the significance threshold of p=0.05, and solid verticallines show the best cut-offs. For the Kaplan-Meier curves, the numbersin the legend show the number of patients in each group and numbers inthe bracket show the number of disease-specific deaths.

FIG. 10. Kaplan-Meier curves to illustrate the disease-specific survivalprobabilities of patient groups in in two TNBC cohorts stratified bynine immune signatures.

The signatures were dichotomised using a cut-off selected over a rangeof percentiles based on Cohort 2 (the left and right columns) and testedin Cohort 1 (the middle column). Dashed lines in the plots on the leftmarks the significance threshold of p=0.05, and solid vertical linesshow the best cut-offs. For the Kaplan-Meier curves, the numbers in thelegend show the number of patients in each group and numbers in thebracket show the number of disease-specific deaths.

FIG. 11. Scatter plots to show correlation between ITLR and expressionof ITLR-associated genes in TNBC.

FIG. 12. Compare the prognostic value of top 100 ITLR-associated genesand ITLR by including both in multivariate Cox analysis model, one geneat a time.

Each point denotes analysis for one gene, plotted values are log(logrank p-value) for the analysis.

FIG. 13. Kaplan-Meier curves illustrating differences indisease-specific survival of TNBC patients stratified with other knownparameters including PAM50 (Perou et al., 2000), pathological assessmentof lymphocytic infiltration (LI), tumour size, and grade.

FIG. 14. Kaplan-Meier curves illustrating differences in 5-year overallsurvival of ovarian cancer patients of patient groups stratified byITLR, by “Lym” (Yuan et al., 2012), by lymPath (lymphocyte abundanceassessed by pathologist), tumour grade, histologic type, or tumourstaging.

FIG. 15. A. Histogram showing clustering of cells from TNBC tumours intocluster having relatively high CTLA4 expression and cluster havingrelatively low CTLA4 expression. B. Kaplan-Meier curve illustratingdifference in survival between patients having low CLTA4 expression andhigh CTLA4 expression.

DETAILED DESCRIPTION OF THE INVENTION

Certain aspects and embodiments of the invention will now be illustratedby way of example and with reference to the figures described above.

The inventor has devised a novel way of statistically modelling thespatial heterogeneity of lymphocytes in tumours, which enablesdetermination of a quantitative measurement of immune infiltration(ITLR). ITLR (intra-tumour lymphocyte ratio) is the ratio ofintra-tumour lymphocytes to cancer cells in a tumour. This quantitativemeasurement of immune infiltration (ITLR) has improved predictive powerin cancer prognosis compared with previous indicators of immuneinfiltration. This measurement of immune infiltration was developedbased on a study of tumours from triple negative breast cancer (TNBC)patients from the METABRIC dataset, but is more generally useful in andapplicable to other breast cancer sub-types and other cancer types. Thegeneralizable nature of ITLR is demonstrated by the data herein showingthat ITLR is also a prognostic indicator in ovarian cancer.

Described herein is the first study to statistically identify categoriesof lymphocytes based on tumour spatial heterogeneity and demonstratetheir clinical implications using samples from a large number ofpatients. This enables a way of modelling spatial heterogeneity intumours which addresses the need for measuring heterogeneity oflymphocytic infiltration in tumours. The ability to generatereproducible, quantitative scores provides new opportunities forincorporating immune infiltration into staging of cancer (i.e. gradingof tumours), as in the use of immunoscore for colorectal cancer (Galon,2014).

The present invention provides a method of measuring immune infiltrationin tumours. In particular, there is provided a method of determining anobjective measurement of immune infiltration in a tumour (ITLR), whichmeasurement is the ratio of intra-tumour lymphocytes to cancer cells.

Accordingly, an aspect of the present invention provides a method ofmeasuring immune infiltration in a tumour, the method comprising:

-   -   providing an image of the tumour in which lymphocytes and cancer        cells have been identified;    -   obtaining a lymphocyte-to-cancer measurement for each        lymphocyte;

classifying a subset of the lymphocytes as intra-tumour lymphocytesaccording to their lymphocyte-to-cancer ratio;

-   -   quantifying the intra-tumour lymphocytes and the cancer cells in        the tumour image;

calculating the intra-tumour lymphocyte ratio (ITLR) as the ratio ofintra-tumour lymphocytes to cancer cells, wherein the ITLR is ameasurement of immune infiltration in the tumour.

The methods described herein may be performed using a tumour image inwhich lymphocytes and cancer cells have been identified. The lymphocytesand cancer cells may have been identified by automated image analysis.

The methods described herein may further comprise a step of identifyingcancer cells and lymphocytes in a tumour image by automated imageanalysis. The methods may comprise steps of generating a tumour imageand then identifying lymphocytes and cancer cells in the tumour image byautomated image analysis.

The step of identifying cancer cells and lymphocytes in a tumour byautomated image analysis may be based on the different nuclearmorphologies of cancer cells and lymphocytes. This step may be performedon tumour sections, such as whole-tumour section slides. The tumoursection may be H&E stained. The types and/or spatial locations of atleast about 10,000 cells may be recorded in this step. The types and/orspatial locations of at least about 20,000, at least about 50,000, atleast about 90,000, at least 100,000, at least about 110,000, about10,000 to 150,000, about 50,000 to 120,000, or about 100,000 to 120,000cells may be recorded in this step. The types and/or spatial locationsof, or about 90,000, about 100,000, or about 110,000 cells may berecorded in this step. The cells may be lymphocytes. This step may useany automated image analysis tool capable of identifying lymphocytes andcancer cells. The automated image analysis tool may be the tooldisclosed in Yuan et al, 2012.

The image analysis tool disclosed in Yuan et al, 2012, which is herebyincorporated by reference in its entirety, identifies cancer,lymphocytes and stromal cells encompassing fibroblasts and endothelialcells based on their nuclear morphologies in H&E whole-tumour sectionslides. The main component of this tool is a classifier trained bypathologists over randomly selected tumour regions and validated in 564breast tumours with 90% accuracy. The image analysis tool described inYuan et al classifies cells into three categories: cancer, lymphocyte orstromal based on morphological features using a support vector machine.

The image analysis tool described in Yuan et al, 2012 identified cancercells by their typically large (>10 μm), round nuclei. The stromal classwas trained on spindle-shaped stromal cell nuclei (likely to befibroblasts) and may encompass other stromal cells with similarmorphology, such as endothelial cells. The lymphocyte class was trainedon immune cells with the distinctive morphology of lymphocytes: small(<8 μm), dark nuclei and not much cytoplasm.

The image analysis tool described in Yuan et al was trained using breasttumour images. Automated image analysis tools, such as those describedin Yuan et al 2012, can be trained in cancer types other than breastcancer (including those cancer types and subtypes mentioned herein) inorder to identify lymphocytes and cancer cells in the tumours of othercancer types.

Various automated image analysis tools are known in the art. For examplethe tools described in Failmezger et al (CRImage) particular Janowczyjet al, and Basavanhally et al, which are hereby incorporated byreference in their entirety. Any such tool may be suitable for, oradapted for, use in the methods described herein.

As a result of automated image analysis, the types and spatial locationsof a large number of cells are recorded in every tumour image. Theautomated image analysis may enable the mapping of spatial distributionsof all, or essentially all, cancer cells and lymphocytes within a tumourimage.

Following a step of identifying the cancer cells and lymphocytes usingautomated image analysis, the spatial relationships of lymphocytes andcancer cells are analysed.

The methods described herein comprise a step of obtaining alymphocyte-to-cancer measurement for each lymphocyte. This provides aquantitative measurement of each lymphocyte's proximity to cancer cellsand spatial location relative to cancer cells.

The step of obtaining a lymphocyte-to-cancer measurement for eachlymphocyte may be carried out using the statistical pipeline exemplifiedin FIG. 1B. First, to globally profile the spatial distribution of thecancer cells, the cancer cell density is quantified, for example using akernel estimate (Hastie et al, 2001). Alternatively, a mean shiftestimate (Cheng, 1995) or scale space (Witkin, 1983) estimate may beused. This builds a ‘cancer landscape’ where hills indicate tumourregions densely populated with cancer cells. The height of a hill thuscorrelates with cancer density (tumour density) at a specific locationin the tumour (FIG. 1B). Secondly, for every lymphocyte, its spatialproximity to cancer is directly quantified with the cancer densitylandscape at its specific location to give a “lymophocyte-to-cancer”measurement for each lymphocyte. Thus a quantitative measurement of thespatial proximity to cancer cells is obtained for each lymphocyte (FIG.1B).

In the studies described herein (see Experimental), cancer cells andlymphocytes were identified, and then their spatial relationships werequantified using a kernel density method. Then, using unsupervisedlearning, three categories of lymphocytes (intra-tumour, adjacent-tumourand distal tumour) were identified based on their spatial proximitiesand spatial positioning relative to cancer cells. These lymphocytecategories are consistent with a pathological quantification scheme thatconsiders intratumoral, adjacent stroma and distant stroma compartments(Mahmoud, 2011). Statistically, these clusters are stable, reported asthe optimal clustering solution 97% of the time upon repeated sampling.

Accordingly, the methods described herein may comprise a step ofobtaining a lymphocyte-to-cancer measurement for each lymphocyte byusing a density estimate, such as a kernel estimate, to model thespatial distribution of the cancer cells. The method then comprises astep of determining the proximity of each lymphocyte to cancer bydetermining the cancer cell density at the location of each lymphocyte,to give a lymphocyte-to-cancer measurement for each lymphocyte. Thelymphocytes are then clustered according to their lymphocyte-to-cancermeasurements. An unsupervised learning method, such as Gaussian mixtureclustering, may be used to cluster lymphocytes according to theirproximity to cancer. The number of clusters may be 2, 3, 4 or more.

In the TNBC study described herein (see Experimental), when lymphocyteswere clustered according to their lymphocyte-to-cancer measurements thenumber of clusters was three (k=3), corresponding to intra-tumourlymphocytes (ITL), adjacent tumour lymphocytes (ATL) and distal tumourlymphocytes (DTL).

In TNBC, lymphocytes having a lymphocyte-to-cancer measurement above thethreshold value of 0.10507473 were classified as ITLs, lymphocyteshaving a lymphocyte-to-cancer measurement below the threshold value of0.10507473 and above the threshold value of 0.03662728 were classifiedas ATLs, and lymphocytes having a lymphocyte-to-cancer measurement belowthe threshold value of 0.03662728 were classified as DTLs. Indetermining the ITLR, the important distinction is between intra-tumourlymphocytes (ITLs) and non intra-tumour lymphocytes (non-ITLs). Thus inTNBC, lymphocytes having a lymphocyte-to-cancer measurement equal to orabove the threshold value of 0.10507473 were classified as ITLs, and theremaining lymphocytes were classified as non-ITLs.

In the ovarian cancer study described herein, when lymphocytes wereclustered according to their lymphocyte-to-cancer measurements thenumber of clusters was two (k=2), corresponding to intra-tumourlymphocytes and non-intra-tumour lymphocytes.

In ovarian cancer, lymphocytes having a lymphocyte-to-cancer measurementabove the threshold value of 0.03114299 were classified as ITLs.Lymphocytes having a lymphocyte-to-cancer measurement below thisthreshold value were classified as non-ITLs.

As an alternative to using cancer density at a lymphocyte location togive a lymphocyte-to-tumour measurement that is indicative of lymphocyteproximity to cancer (i.e. lymphocyte closeness to cancer), the step ofobtaining a lymphocyte-to-cancer measurement for each lymphocyte may becarried out based on a distance measure between a lymphocyte and one ormore cancer cells, such as the Euclidean distance. The lymphocytes arethen clustered according to their lymphocyte-to-cancer measurements, asdescribed above, for example using an unsupervised learning method, suchas Gaussian mixture clustering. In this context, where thelymphocyte-to-cancer measurement is indicative of distance from (ratherthan proximity to) cancer, a lymphocyte may be classified as an ITL ifit has lymphocyte-to-cancer measurement below a threshold value.

The methods described herein may comprise classifying lymphocytes asintra-tumour lymphocytes. That is, the methods may comprise classifyinga subset of cells identified as lymphocytes in the tumour image asintra-tumour lymphocytes. Classifying lymphocytes may comprisedetermining whether the lymphocyte-to-cancer measurement is above acertain threshold value. The threshold value, for example in TNBC, maybe around 0.1, around 0.105 or around 0.10507473. The threshold value,for example in ovarian cancer, may be around 0.03, around 0.0311, oraround 0.03114299.

The methods described herein may comprise determining a threshold valuefor a lymphocyte-to-cancer measurement, for use in classifying alymphocyte as an intra-tumour lymphocyte or a non-intra-tumourlymphocyte. For example, where the lymphocyte-to-cancer measurement isindicative of lymphocyte proximity to cancer, the lymphocyte may beclassed as an intra-tumour lymphocyte if it has a lymphocyte-to-cancermeasurement above the lymphocyte-to-cancer measurement threshold value.Determining a threshold value for a lymphocyte-to-cancer measurement maycomprise determining lymphocyte-to-cancer measurements for a populationof lymphocytes and clustering the lymphocytes by unsupervised learning,and taking the minimum value of the most cancer proximal cluster (thecluster with the highest measurements) as the threshold value forclassifying intra-tumour lymphocytes. A lymphocyte may be classified asan intra-tumour lymphocyte if it has a lymphocyte-to-cancer measurementabove (or equal to or above) the threshold value.

Determining the threshold value may further comprise testing thestability of the clustering by sampling the population of lymphocytes,clustering the sampled population of lymphocytes and determining thatthe cluster solution (k=x where x is the number of clusters) is stable.The number of clusters is stable where k for the sampled population isthe same for 200 repeated samples at least 90%, at least 95% or at least97% of the time.

Furthermore, the inventor has shown significant differences betweenlymphocyte categories both in spatial distance to the nearest cancercell and spatial positioning of surrounding cancer cells, supportingtheir biological relevance. For instance, in the presently disclosedstudy of tumours from TNBC patients from the METABRIC dataset, anintra-tumour lymphocyte is on average 7 μm away from a cancer cell and 3μm from the centroid of convex hull region formed by nearby cancercells. An adjacent-tumour lymphocyte may be also close to the nearestcancer cells but would be further away from the centroid of convex hullregion because it is not surrounded by cancer cells. Thus, the newclassification approach disclosed herein is based on spatial measuresthat account for spatial positioning of cancer cells whilst beingcomputationally efficient enough to analyse whole-tumour sections.Compared to a previously reported measure of lymphocyte abundance as adirect output from image analysis (Yuan, 2012), an advantage of this newapproach is that it accounts for the spatial heterogeneity of immuneinfiltration, which is recognised as an important property of immuneinfiltration (Galon, 2006) but rarely quantitatively analysed.

Following the step of classifying lymphocytes as intra-tumourlymphocytes, the ratio of intra-tumour lymphocytes to cancer cells iscalculated. This ratio is the ITLR (the intra-tumour lymphocyte ratio),which is an objective and quantitative measurement of immuneinfiltration in tumours. The ITLR is the ratio of intra-tumourlymphocytes to cancer cells in the tumour expressed as a decimalfraction. For example, an ITLR of 0.011 represents a 1.1% ofintra-tumour lymphocytes to cancer cells i.e. a ratio of 11 intra-tumourlymphocytes to 1000 cancer cells.

The inventor has shown that ITLR is a robust and powerful prognosticindicator in triple negative breast cancer (TNBC), as discussed below,and also in ovarian cancer. Since immune infiltration is implicated inmany cancer types, as discussed in more detail below, including breastcancer, ovarian cancer, colorectal cancer (Galon, 2014), melanoma andnon-small cell lung cancer, ITLR may also be used as a prognosticindicator in various cancer types.

For prognosis in TNBC, the ITLR cut-off of 0.011 was selected based ontumour images from the METABRIC cohort. Patients whose tumours had anITLR below the cut-off value of 0.011 had a significantly worse clinicaloutcome in terms of disease-specific survival compared with patientswhose tumours had an ITLR above the cut-off value.

For prognosis in ovarian cancer, the ITLR cut-off of 0.06086 wasselected based on tumour images from an unpublished tumour cohort.Patients whose tumours had an ITLR below the cut-off value had asignificantly worse clinical outcome in terms of overall survivalcompared with patients whose tumours had an ITLR above the cut-offvalue.

An aspect of the present invention provides a method of determining acut-off value for ITLR for use in determining a prognosis in cancer,wherein an ITLR below the cut-off value indicates a poor prognosis. Themethod comprises determining the ITLR for a plurality of tumours,wherein each tumour is from a respective cancer patient in a cohort ofcancer patients, and selecting a cut-off value for the ITLR whereinpatients with an ITLR equal to or below the cut-off value have asignificantly worse prognosis compared with patients with an ITLR abovethe cut-off value.

Accordingly, an aspect of the present invention provides a method ofdetermining an ITLR cut-off value for a cancer type or subtype, for usein providing a prognosis in a cancer patient having that cancer type orsubtype, the method comprising:

-   -   measuring immune infiltration in a tumour from each member of a        cohort of cancer patients having the cancer type or subtype        according to the method of claim 1, thereby calculating the ITLR        for each tumour;    -   relating the ITLR for each tumour to the clinical outcome of        each cancer patient in the cohort of cancer patients; and    -   selecting a cut-off value for ITLR, wherein an ITLR equal to or        below the cut-off value is associated with a significantly        different clinical outcome in the cohort of cancer patients than        an ITLR above the cut-off value.

An ITLR equal to or below the cut-off value may be associated with asignificantly worse clinical outcome than an ITLR above the cut-offvalue. An ITLR equal to or below the cut-off value may be associatedwith a significantly better clinical outcome than an ITLR above thecut-off value.

The selection of the cut-off value for ITLR serves to dichotomise thecontinuous range of ITLR values for the tumour images from a patientcohort. The ITLR cut-off value is selected such that there is asignificant difference in clinical outcome between patients with an ITLRbelow the cut-off and patients with an ITLR above the cut-off value. Ingeneral, the ITLR cut-off value is selected such that patients having anITLR below or equal to the cut-off value (i.e. patients having a tumourwith an ITLR below or equal to the cut-off value) have a significantlyworse prognosis than patients having an ITLR that is above the cut-offvalue (i.e. patients having a tumour with an ITLR equal to or above thecut-off value).

In the context of the present invention a significant difference inprognosis refers to a clinical outcome that is significantly differentaccording to the Log rank test. Preferably p<0.0500, p<0.0250, p<0.0100,p<0.0090, p<0.0065, p<0.0010, or p<0.0001 according to the log ranktest.

Selection of the ITLR cut-off value may comprise identifying an ITLRvalue wherein about 20% to 80% of the patient cohort has an ITLR belowthat value. Selection of the ITLR cut-off value may comprise identifyingan ITLR value wherein about 20% to 80% of the patient cohort has an ITLRbelow that value and wherein patients having an ITLR below the cut-offvalue have a significantly worse prognosis than patients having an ITLRthat is above the cut-off value.

The clinical outcome may be disease-specific survival, disease freesurvival, overall survival, relapse-free survival, progression-freesurvival, survival rate or survival time. The clinical outcome may bedisease-specific survival. Disease-specific survival may be defined withtime as maximum 5 years or 10 years from diagnosis and event as deathdue to cancer (the 5 year disease specific survival and 10 year diseasespecific survival respectively). Overall survival may be defined withtime as maximum 5 years or 10 years from diagnosis and event as deathdue to any cause. Relapse-free survival may be defined with time asmaximum 10 years from diagnosis and event as tumour relapse. A poorprognosis refers to a prediction of a poor clinical outcome, whereas apositive prognosis refers to a prediction of a positive clinicaloutcome.

The methods described herein may use a cohort of cancer patients fromThe Cancer Genome Atlas (TOGA) as the “discovery” cohort. This dataset,with its H&E and matched molecular profiling data will be an extremelyuseful cohort to validate the utility of ITLR and to select and refineITLR cut-off values for use in prognostic and/or therapeutic methods.TCGA has chosen cancers for study based on criteria that include poorprognosis and overall public health impact and the availability of humantumour and matched-normal tissue samples that meet TCGA standards forpatient consent quality and quantity.

The experiments disclosed herein show the utility of ITLR in TNBC and inovarian cancer. ITLR is a generalizable measure for ITLs and willtherefore be useful as a measure of immune infiltration in other cancertypes I subtypes, especially given that manual assessment of ITLS hasreported value in many cancer types I subtypes.

As already mentioned above, immune infiltration is implicated in manycancer types, including breast cancer, ovarian cancer, colorectalcancer, melanoma and non-small cell lung cancer, ITLR may also be usedas a prognostic indicator in various cancer types.

Immune infiltration is implicated in many cancers including breastcancer (including breast ductal carcinoma breast and breast lobularcarcinoma) (Dieci 2014; Loi S 2013; Kruger J M 2013; Liu S, 2012;Ascierto M L 2012, Rody A, 2011; Mahmoud S M A, 2011; Denkert C, 2010;Ueno T, 2000) central nervous system cancer (including glioblastomamultiforme and lower grade glioma) (Kmiecik J, 2013; Yang I, 2010;McNamara M G, 2014; Crane C A, 2014; Bambury R M; Alexiou G A, 2013;Vauleon E, 2013) endocrine cancer (including adrenocortical carcinoma,papillary thyroid carcinoma, paraganglioma & pheochromocytoma)(Papewalis C; Huang C T; Mukherji B) gastrointestinal cancer (includingCholangiocarcinoma, Colorectal Adenocarcinoma, Liver HepatocellularCarcinoma, Pancreatic Ductal Adenocarcinoma, Stomach-Esophageal Cancer)(Kono K, 20116; Wu G; Gao Q; Hiraoka N) gynecologic cancer (includingCervical Cancer (Zhang Y, 2014; Ancuta E, 2009), Ovarian SerousCystadenocarcinoma (Townsend K N, 2013; Milne K 2009; Clarke B, 2009),Uterine Carcinosarcoma, Uterine Corpus Endometrial Carcinoma) (Ohno S,2004) head and neck cancer (including Head and Neck Squamous CellCarcinoma, Uveal Melanoma) (Spanos W C, 2009; Pretscher D, 2009)hematologic cancer (including Acute Myeloid Leukemia, thymoma, lymphoma)(Yong A S, 2011; Dave S S, 2004;) skin cancer (including CutaneousMelanoma) (Tjin E P, 2014; Erdag G, 2012; Bystryn J C, 1992; Halliday G,1995) soft tissue cancer (including Sarcoma) (Kim J R, 2016; Sorbye S W2011; Fiorelli V, 1998) thoracic cancer (including Lung Adenocarcinoma,Lung Squamous Cell Carcinoma, Mesothelioma) (Suzuki K, 2013; Welsh T J,2005; Villegas F R, 2002; Hegmans J P, 2006; Dieu-Nosjean M C) urologiccancer (including Chromophobe Renal Cell Carcinoma, Clear Cell KidneyCarcinoma, Papillary Kidney Carcinoma, Prostate Adenocarcinoma,Testicular Germ Cell Cancer, Urothelial Bladder Carcinoma) (Davidsson S,2013; Thompson R H, 2007; Webster W S, 2006; Gannon P O, 2009; SjodahlG, 2014).

The methods of the present invention may be applied in any of the cancertypes or subtypes mentioned above.

ITLR is an objective quantitative indicator of lymphocytic infiltrationin tumours. The inventor has shown the importance of using such aquantitative measurement of lymphocytic infiltration in predictingclinical outcome in cancer.

ITLR is a new spatial and quantitative measure of intra-tumourlymphocytes (ITLs). This measure is a consistent, stable and independentpredictor of disease-specific survival across two independent cohorts of181 TNBC patients in total. This measurement may use a cut-off of 0.011(1.1% of intra-tumour lymphocytes to cancer cells) that dichotomises theITLR score. The 20% of TNBC patients with ITLR scores lower than thiscut-off have significantly worse disease-specific survival than patientswith higher scores, and this association is independent of standardclinical parameters. Taken together, these data support the utility ofITLR as a prognostic biomarker for cancer, including TNBC. Accordingly,disclosed herein is an objective and fully automated scoring system forthe standardised assessment of immune infiltration that can be used inthe context of clinical trials and subsequently aid the treatmentdecision making process.

Accordingly, an aspect of the present invention may further compriseusing ITLR as a prognostic biomarker. The method may comprise measuringthe ITLR of a tumour from a cancer patient and using the ITLR todetermine a prognosis for the patient. The method may comprisedetermining the ITLR in a tumour from a cancer patient and using theITLR to determine a prognosis for the patient, wherein an ITLR below apredetermined cut-off value indicates a poor prognosis. The method maycomprise determining the ITLR in a tumour from a cancer patient andusing the ITLR to determine a prognosis for the patient, wherein an ITLRabove a predetermined cut-off value indicates a poor prognosis.

In particular, an aspect of the present invention provides a method ofproviding a prognosis in a cancer patient, the method comprising:

-   -   measuring immune infiltration in a tumour from the cancer        patient according to a method described herein, thereby        calculating the ITLR for the tumour,    -   wherein an ITLR below a predetermined ITLR cut-off value        indicates a poor prognosis.

An aspect of the present invention provides a method of providing aprognosis in a cancer patient, the method comprising:

-   -   providing an image of the tumour in which lymphocytes and cancer        cells have been identified;    -   obtaining a lymphocyte-to-cancer measurement for each        lymphocyte;    -   classifying a subset of the lymphocytes as intra-tumour        lymphocytes according to their lymphocyte-to-cancer ratio;    -   quantifying the intra-tumour lymphocytes and the cancer cells in        the tumour image;    -   calculating the intra-tumour lymphocyte ratio (ITLR) as the        ratio of intra-tumour lymphocytes to cancer cells, wherein the        ITLR is a measurement of immune infiltration in the cancer        patient's tumour    -   and wherein an ITLR below a predetermined ITLR cut-off value        indicates a poor prognosis.

The inventor has shown that ITLR is an independent predictor of clinicaloutcome in cancer. That is, the ITLR is predictive of clinical outcomewithout using any other biomarker (such as a gene expression biomarker)or clinical indicator (such as tumour size). For example, the inventorhas shown that ITLR is an independent predictor of clinical outcome intriple negative breast cancer (TNBC) and in ovarian cancer. For examplein the studies described herein there was no correlation between ITLRand tumour size, node status and TP53 mutation status (FIG. 4D), and soITLR is independent of such clinical indicators and biomarkers.Preferably, if the ITLR is below a predetermined cut-off value (or equalto or below a predetermined cut-off value), this indicates a poorprognosis (i.e. a poor clinical outcome). A poor prognosis, or poorclinical outcome, may be poor disease-specific survival.

In the present context, an ITLR below a predetermined cut-off value maybe referred to as a low ITLR, or ITLR-low. Conversely an ITLR above apredetermined cut-off value may be referred to as a high ITLR, orILTR-high. A predetermined cut-off value for an ITLR may simply bereferred to herein as an ITLR cut-off value.

A poor prognosis means that the patient has a worse prognosis than apatient having an ITLR value above the cut-off ITLR value. For example apoor prognosis may mean that the patient is expected to have shorterdisease-specific survival time than a patient having an ITLR value abovethe cut-off ITLR value. A poor prognosis may mean that the patient has aworse prognosis than a patient having an ITLR value above the cut-offITLR value. The hazard ratio between the patient group having an ITLRbelow the ITLR cut-off value and the group having an ITLR above the ITLRcut-off value may be from around 0.2 to around 0.4, may be from around0.25 to around 0.36, may be around 0.25 or may be around 0.36. Thismeans that a patient with ITLR high than the cut-off value is 0.25-0.36times less likely to die from breast cancer than a patient with ITLRlower than the cut-off value. A poor prognosis may mean that a patienthas a survival probability of around 50%, or around 49%, five years fromdiagnosis, or ten years from diagnosis. A good prognosis may mean that apatient has a survival probability of around 80% five years fromdiagnosis or ten years from diagnosis.

A predetermined ITLR cut-off value may be about 0.011, or about 0.061.The cut-off value may be from about 0.005 to about 0.070, from about0.010 to about 0.070, from about 0.010 to about 0.012, or from about0.050 to about 0.070. A predetermined ITLR cut-off value for TNBC may beabout 0.011, and for ovarian cancer may be about 0.061.

ITLR was tested in two independent cohorts of TNBC and shown to bepredictive of disease-specific survival. When TNBC Cohort 1 was used asthe discovery cohort an ITLR cut-off value of 0.011 was selected (thatis, patients having an ITLR below this value showed significantly worsedisease-specific survival than patients having an ITLR above thisvalue), and in Cohort 2 an ITLR of below 0.011 was associated withsignificantly worse disease-specific survival than patients with an ITLRabove 0.011 (Log-rank test p=0.0063, FIG. 4F). Similarly, when TNBCCohort 2 was used as the discovery cohort an ITLR cut-off of 0.011 wasselected, and in Cohort 1 an ITLR of below 0.011 was associated withsignificantly worse disease-specific survival than patients with an ITLRabove 0.011 (p=0.0037, FIG. 4F).

The prognostic power of ITLR compares favourably with that of previouslypublished prognostic indicators. ITLR is a more powerful prognosticindicator than the previously published indicator “Lym” (a tumoursection image-analysis based indicator of lymphocyte abundance; Yuan etal., 2012) and several published gene signature-based indicators(Calabro et al., Ascierto et al., Rody et al, Ma et al., Gu-Trantien etal).

The same cut-off selection approach used to select the ITLR cut-off wasused to test the prognostic power of “Lym” (an image-based measure oflymphocyte abundance in tumour sections) and several gene expressionsignatures in TNBC and in ovarian cancer. None of these other prognosticindicators consistently correlated with prognosis in both Cohort 1 andCohort 2. By contrast, ITLR consistently stratified patients into twogroups of different clinical outcome. (See FIG. 4, FIG. 5, FIG. 14)

Compared to published gene expression signatures, ITLR was also the onlysignature to show significant correlation with disease-specific survivalin multivariate Cox proportional hazards model together with standardclinical parameters of nodal status and tumour size in both cohorts,whichever cohort was used as the discovery cohort (Tables 1 to 3).

Using samples from both TNBC cohorts, ITLR has a log-rank p-value of2.1×10⁻⁴ and HR 0.32 (0.17-0.58). To test the robustness of the Coxmodel in determining the prognostic value of ITLR, bootstrap analysiswas used in randomly perturbed data and the univariate and multivariateregression analysis was repeated 1,000 times. In 95.6% and 94.7% ofinstances, ITLR remained significantly associated with prognosis inunivariate and multivariate analysis, respectively. Taken together,these data support the stability and robustness of ITLR as anindependent prognostic biomarker in TNBC.

ITLR measures the ratio of intra-tumour lymphocytes to cancer cells,thus is different to the pathological assessment approach described inprevious studies (Denkert, 2010; Loi, 2013; Deici, 2014), where theproportion of tumour nests that were infiltrated by lymphocytes werereported. These previous studies agree with the results describedherein, because they show that tumour-infiltration lymphocytes aresignificantly correlated with favorable outcome in TNBC. These previousapproaches, like the experiments reported herein, were based on H&Estained pathological samples and therefore support the position thatmeasures of lymphocytic infiltration can be useful tool to aid clinicaldecisions in TNBC.

Unlike the methods of the invention (which are based on automated imageanalysis), the previous methods are based on assessment of tumoursections by pathologists (Denkert, 2010; Loi, 2013; Deici, 2014;Salgado, 2014). The previous methods looking at proportions of tumournests infiltrated by lymphocytes are thus subjective, and thereforesubject to bias and variability, and generate results relatively slowlywith higher associated costs, the previous methods are thus unsuitablefor very large scale analyses.

The approach taken in the present invention, of identifying lymphocytesubtypes by image analysis, contrasts with previous approaches toassessing immune infiltration in tumours. Previous approaches usingimage analysis (Yuan, 2012) have only taken account of abundance oflymphocytes in tumours, whereas approaches that attempt to take accountof spatial locations of lymphocytes (Denkert, 2010; Loi, 2013; Deici,2014) have used only manual (pathologist) based processes and haverelied on qualitative and subjective assessment of cancer cellconstellations and their relationships with lymphocytes (the presence ofcancer cell “nest” and the proportion of such nests containinglymphocytes). By contrast the present inventor has taken the approach ofusing image analysis techniques to identify lymphocyte subtypes withintumours and use the relative abundance of a subtype of lymphocytes(ITLs) to cancer cells as an objective quantitative measure of immuneinfiltration. The image analysis techniques of the present invention arepreferably automated or computer-implemented techniques, therebyfacilitating analysis of large numbers (in the order of100,000—preferably at least 10,000, at least 50,000, or at least100,000) of lymphocytes per tumour image and permitting large-scaleanalyses of cohorts of patients having various types and subtypes ofcancer.

Unlike the methods of the invention, which robustly predict clinicaloutcome, pathological scores of immune infiltration (includingpathological assessment of lymphocytic infiltration) were notsignificantly correlated with prognosis (FIG. 13). The pathologicalscores tested included PAM50 (Perou et al., 2000), pathologicalassessment of lymphocytic infiltration, tumour size, and grade.Pathological assessment of lymphocytic infiltration for the purposes ofthis study was was scored as absent, mild, or severe: Absent if therewere no lymphocytes, mild if there was a light scattering oflymphocytes, and severe if there was a prominent lymphocytic infiltrate.

The prognostic methods of the invention, which are based on an objectiveindicator of immune cell infiltration obtained by an automated method,have several advantages over previous prognostic methods for use incancer. As explained above, ITLR has greater predictive power thanseveral previously known cancer biomarkers and prognostic indicators andgreater predictive power than pathological scores of immuneinfiltration. Because ITLR is determined using automated methods itprovides an objective measurement of immune cell infiltration in cancer(i.e. not subject to subjective bias or human error, which causesvariability in results), it requires no pathologist scoring (andtherefore no pathologist training or following of new guidelines) and isrelatively low cost and quick to obtain, which makes it suited to largescale analysis of cancer data. Although detection of gene-expressionsignature based signatures may be automated, ITLR, because it canconveniently be based on tumour images such as H&E stained sections(copies of which are easily and cheaply shared and stored long-term), islower cost and more convenient biomarker than gene expressionsignature-based biomarkers (which require access to preserved biologicalsamples). The image-based ITLR outperforms several gene expression-basedsignatures using the optimal cut-off selection method. In addition,considering the cost of microarray data acquisition, the ITLR-basedapproaches described herein open a new avenue for large-scale analysison readily available pathological samples.

TABLE 1 Univariate and multivariate Cox regression results for ITLR andother signatures in two TNBC cohorts. Cohort 1 Cohort 2 HR(CI) p Conc pHR(CI) Conc ITL Uni- 0.36(0.17-0.77) 0.0063 0.601 0.25(0.09-0.69) 0.00360.659 ITL 0.32(0.15-0.7)  0.0042 0.668 0.15(0.05-0.43) 0.00051 0.76 Node0.63(0.29-1.4)  0.26  4.93(1.61-15.08) 0.0052 Size 2.62(1.27-5.41)0.0092 2.07(0.9-4.74)  0.087 Lym Uni- 0.47(0.21-1.02) 0.051 0.5740.41(0.12-1.43) 0.15 0.575 Lym 0.48(0.22-1.05) 0.066 0.6560.23(0.05-1.02) 0.053 0.735 Node 0.69(0.32-1.5)  0.35  4.65(1.46-14.81)0.0092 Size 2.35(1.16-4.77) 0.018 1.66(0.65-4.25) 0.29 Calabro Uni-0.25(0.12-0.52) 5.2 × 10 ⁻⁵ 0.66  0.5(0.18-1.39) 0.18 0.587 Calabro0.27(0.13-0.56) 3.8 × 10 ⁻⁴ 0.703 0.41(0.14-1.19) 0.1 0.744 Node0.75(0.35-1.6)  0.45  4.57(1.45-14.37) 0.0093 Size 2.26(1.07-4.76) 0.0321.91(0.82-4.46) 0.13 Ascierto Uni- 0.34(0.15-0.77) 0.0066 0.6211.23(0.4-3.83)  0.72 0.51 Ascierto 0.39(0.17-0.88) 0.024 0.6711.18(0.37-3.72) 0.78 0.735 Node 0.85(0.39-1.84) 0.68  3.6(1.21-10.7)0.021 Size 2.06(1.02-4.16) 0.044 2.16(0.86-5.45) 0.1 IL8 Uni-3.09(1.46-6.51) 0.0018 0.615 0(0-Inf)   0.0099 0.645 IL8 2.79(1.32-5.92)0.0073 0.679 0(0-Inf)   1 0.808 Node 0.81(0.37-1.75) 0.593.14(1.06-9.34) 0.039 Size 2.23(1.08-4.63) 0.031 1.75(0.71-4.28) 0.22CXCL13 Uni- 0.21(0.1-0.46)  1.5 × 10 ⁻⁶ 0.69 0.76(0.28-2.1)  0.6 0.545CXCL13 0.24(0.11-0.54) 4.5 × 10 ⁻⁴ 0.721 0.83(0.29-2.37) 0.73 0.739 Node0.69(0.32-1.49) 0.35  3.61(1.22-10.71) 0.021 Size 1.71(0.83-3.55) 0.152.12(0.86-5.22) 0.1 Shaded sections show results from multivariateregression. Uni-: Univariate Cox regression; HR: Hazard Ratio; CI: lowerand higher 95% Confidence Interval; Conc: Concordance; 0(0-Inf): wherethe Cox model failed to converge. P-values that pass the significantthreshold of 0.05 are shown in bold.

TABLE 2 Univariate and multivariate Cox regression results for ITLR andother eight signatures using the optimal cut-offs selected in Cohort 1and validated in Cohort 2. Cohort1 Cohort2 HR(CI) p-value conc HR(CI)p-value conc Uni-ITL 0.36(0.17-0.77) 0.0063 0.601 0.25(0.09-0.69) 0.00360.659 Multi-ITL 0.32(0.15-0.7)  0.0042 0.668 0.15(0.05-0.43) 0.000510.76 Multi-node 0.63(0.29-1.4)  0.26  4.93(1.61-15.08) 0.0052 Multi-size2.62(1.27-5.41) 0.0092 2.07(0.9-4.74)  0.087 Uni-Lym 0.47(0.21-1.02)0.051 0.574 0.41(0.12-1.43) 0.15 0.575 Multi-Lym 0.48(0.22-1.05) 0.0660.656 0.23(0.05-1.02) 0.053 0.735 Multi-node 0.69(0.32-1.5)  0.35 4.65(1.46-14.81) 0.0092 Multi-size 2.35(1.16-4.77) 0.0181.66(0.65-4.25) 0.29 Uni-Calabro 0.25(0.12-0.52) 5.20E−05 0.66 0.5(0.18-1.39) 0.18 0.587 Multi-Calabro 0.27(0.13-0.56) 0.00038 0.7030.41(0.14-1.19) 0.1 0.744 Multi-node 0.75(0.35-1.6)  0.45 4.57(1.45-14.37) 0.0093 Multi-size 2.26(1.07-4.76) 0.0321.91(0.82-4.46) 0.13 Uni-IL8 3.09(1.46-6.51) 0.0018 0.615 0(0-Inf)  0.0099 0.645 Multi-IL8 2.79(1.32-5.92) 0.0073 0.679 0(0-Inf)   1 0.808Multi-node 0.81(0.37-1.75) 0.59 3.14(1.06-9.34) 0.039 Multi-size2.23(1.08-4.63) 0.031 1.75(0.71-4.28) 0.22 Uni-Bcell  0.6(0.25-1.48)0.26 0.557 0.51(0.12-2.27) 0.37 0.539 Multi-Bcell 0.57(0.23-1.4)  0.220.655 0.48(0.11-2.17) 0.34 0.747 Multi-node 0.7(0.32-1.5) 0.353.77(1.27-11.2) 0.017 Multi-size 2.38(1.19-4.76) 0.014 2.07(0.86-5)  0.11 Uni-Bcell.IL8 0.52(0.24-1.11) 0.086 0.581  1.1(0.41-2.95) 0.860.482 Multi-Bcell.IL8 0.53(0.25-1.12) 0.097 0.648 1.22(0.42-3.53) 0.710.743 Multi-node 0.74(0.34-1.6)  0.44  3.76(1.24-11.41) 0.02 Multi-size2.36(1.17-4.76) 0.016 2.21(0.88-5.54) 0.091 Uni-Ascierto 0.34(0.15-0.77)0.0066 0.621 1.23(0.4-3.83)  0.72 0.51 Multi-Ascierto 0.39(0.17-0.88)0.024 0.671 1.18(0.37-3.72) 0.78 0.735 Multi-node 0.85(0.39-1.84) 0.68 3.6(1.21-10.7) 0.021 Multi-size 2.06(1.02-4.16) 0.044 2.16(0.86-5.45)0.1 Uni-CXCR3  0.3(0.14-0.64) 9.00E−04 0.618 0.82(0.3-2.25)  0.7 0.535Multi-CXCR3 0.31(0.15-0.66) 0.0026 0.683 0.79(0.25-2.45) 0.68 0.73Multi-node 0.86(0.39-1.87) 0.7  3.81(1.24-11.72) 0.02 Multi-size2.24(1.13-4.44) 0.02 2.07(0.82-5.18) 0.12 Uni-CXCL13 0.21(0.1-0.46) 1.50E−05 0.69 0.76(0.28-2.1)  0.6 0.545 Multi-CXCL13 0.24(0.11-0.54)0.00045 0.721 0.83(0.29-2.37) 0.73 0.739 Multi-node 0.69(0.32-1.49) 0.35 3.61(1.22-10.71) 0.021 Multi-size 1.71(0.83-3.55) 0.15 2.12(0.86-5.22)0.1 Uni-: Univariate Cox regression; Multi-: Multivariate Coxregression; HR: Hazard Ratio; CI: lower and higher 95% ConfidenceInterval; Conc: Concordance; Inf: Cox model failed to converge.

TABLE 3 Univariate and multivariate Cox regression results for ITLR andother eight signatures using the optimal cut-offs selected in Cohort 2and validated in Cohort 1. Cohort1 Cohort2 HR(CI) p-value conc HR(CI)p-value conc Uni-ITL 0.45(0.21-0.96) 0.033 0.587 0.26(0.1-0.71)  0.00480.656 Multi-ITL 0.38(0.17-0.84) 0.016 0.654 0.16(0.05-0.48) 0.001 0.76Multi-node 0.62(0.28-1.37) 0.23  4.64(1.52-14.15) 0.007 Multi-size2.62(1.27-5.39) 0.0088 2.07(0.89-4.83) 0.091 Uni-Lym 0.91(0.43-1.91) 0.80.524 0.35(0.13-0.98) 0.038 0.63 Multi-Lym 0.92(0.43-1.95) 0.82 0.6270.29(0.1-0.85)  0.024 0.778 Multi-node 0.72(0.33-1.58) 0.41 3.82(1.29-11.38) 0.016 Multi-size 2.33(1.18-4.64) 0.015 1.85(0.73-4.73)0.2 Uni-Calabro 0.53(0.24-1.2)  0.12 0.578 0(0-Inf)   0.04 0.608Multi-Calabro 0.56(0.24-1.28) 0.17 0.667 0(0-Inf)   1 0.799 Multi-node0.67(0.31-1.46) 0.31  4.12(1.39-12.23) 0.011 Multi-size 2.17(1.11-4.24)0.023 1.91(0.8-4.55)  0.14 Uni-IL8 1.76(0.86-3.6)  0.12 0.5750.18(0.05-0.63) 0.0026 0.692 Multi-IL8 1.74(0.84-3.59) 0.14 0.650.21(0.06-0.77) 0.018 0.795 Multi-node  0.8(0.37-1.73) 0.57 3.76(1.25-11.34) 0.019 Multi-size 2.29(1.17-4.51) 0.016 1.87(0.68-5.17)0.23 Uni-Bcell 0.74(0.35-1.59) 0.44 0.541 0.41(0.09-1.82) 0.23 0.557Multi-Bcell 0.75(0.35-1.6)  0.45 0.63 0.33(0.07-1.5)  0.15 0.763Multi-node 0.72(0.33-1.55) 0.39  4.24(1.41-12.76) 0.01 Multi-size2.33(1.17-4.61) 0.016 1.94(0.81-4.67) 0.14 Uni-Bcell.IL8 0.82(0.35-1.92)0.65 0.511 0.45(0.16-1.31) 0.13 0.599 Multi-Bcell.IL8 0.75(0.32-1.76)0.51 0.629 0.59(0.2-1.76)  0.34 0.753 Multi-node 0.73(0.34-1.59) 0.433.28(1.08-9.94) 0.036 Multi-size 2.4(1.2-4.83) 0.014 2.18(0.85-5.6) 0.11 Uni-Ascierto 0.83(0.39-1.78) 0.64 0.52 2.26(0.82-6.23) 0.11 0.63Multi-Ascierto   1(0.46-2.16) 0.99 0.619  2.6(0.87-7.82) 0.089 0.773Multi-node 0.73(0.34-1.59) 0.43 3.22(1.08-9.61) 0.036 Multi-size2.33(1.16-4.66) 0.017 2.46(0.91-6.62) 0.075 Uni-CXCR3 0.56(0.24-1.29)0.17 0.569 0(0-Inf)   0.029 0.618 Multi-CXCR3 0.62(0.26-1.48) 0.28 0.6580(0-Inf)   1 0.805 Multi-node 0.68(0.32-1.49) 0.34  4.17(1.41-12.38)0.01 Multi-size 2.16(1.09-4.28) 0.028 1.89(0.8-4.46)  0.15 Uni-CXCL130.35(0.17-0.75) 0.0043 0.605 2.21(0.71-6.86) 0.16 0.595 Multi-CXCL130.38(0.18-0.79) 0.01 0.663  3.38(0.92-12.45) 0.067 0.773 Multi-node0.69(0.32-1.49) 0.35 3.71(1.24-11.1) 0.019 Multi-size 2.29(1.11-4.72)0.026 2.71(0.94-7.8)  0.064 Uni-: Univariate Cox regression; Multi-;Multivariate Cox regression; HR: Hazard Ratio; CI: lower and higher 95%Confidence Interval; Conc: Concordance.

ITLR as an unbiased assessment of immune infiltration can facilitate thediscovery of molecular correlates with this clinically importantphenomenon. While the expression of many immune-related genes in tumourswas significantly associated with ITLR, it is unclear whether thesegenes are expressed on cancer cells or lymphocytes. This is because themicroarray data were obtained using whole-tumour materials withoutmicro-dissection.

The data herein show that the RNA expression of cytotoxicT-lymphocyte-associated protein 4 (CTLA4), a receptor of theimmunoglobulin family and the target of ipilimumab, was significantlyassociated with ITLR as well as longer disease specific survival inTNBC. This is consistent with the recent observation in non-small celllung cancers that over-expression of CTLA4 is associated with reduceddeath rate (Salvi, 2012). CTLA4 is expressed in tumour cells indifferent cancer types (Contardi, 2005). In breast cancer it isexpressed in both tumour cells and T cells, and an inverse correlationbetween CTLA4 expression and clinical outcome (i.e. high CTLA4expression associated with poor clinical outcome) has been previouslyreported in 60 patients with different breast cancer subtypes (Mao,2010), which is in contrast with the data herein from TNBC (see below),and which thus highlights the novel molecular insights into canceryielded by ITLR. A recent study showed that in situ mRNA expression ofanother receptor of the immunoglobulin superfamily, PDL1, is associatedwith increased immune infiltration and favourable recurrence freesurvival across different breast cancer subtypes (Schalper, 2014).

Taken together, the data herein support the potential of CTLA4-targetedtherapies in TNBC. CTLA4 is a negative regulator of T cells, andtherefore its expression reduces T cell-mediated killing of cancercells. The data herein show a positive association between CLTA4expression and ITLR, consistent with ITL expression of CTLA4. Theexpression of CTLA4 in ITLs may explain why in many tumours cancer cellswere not eliminated even in the presence of high numbers of ITLs. Theuse of CTLA4 antagonists to inhibit immune tolerance to cancer and toactivate ITLs may be an effective treatment strategy for TNBC.

Unsupervised clustering with Gaussian Mixture modelling for CTLA4expression in all 1,980 METABRIC tumours revealed two clusters, one withhigh and one with low level of expression of CTLA4 (FIG. 15 A). Usingthis clustering definition for TNBC tumours we found that TNBC patientswith higher level of CTLA4 expression have significantly betterdisease-specific survival than patients with lower level of CTLA4expression (p=0.018, HR=0.61, CI=0.41-0.92, FIG. 15 B).

The gene module analysis also revealed several tightly connected,functionally related modules. For example, one module contains APOBEC3G(Apolipoprotein B MRNA Editing Enzyme, Catalytic Polypeptide-Like 3G),which is known to play important roles in adaptive and innate immunityand has been investigated extensively in viral infection (Mangeat, 2003)but its role in breast cancer has not been investigated in detail. It isa member of the apolipoprotein B mRNA-editing enzyme, catalyticpolypeptide-like editing complex family together with APOBEC3B, whichwas found to be a source of mutagenesis in many major cancer typesincluding breast cancer (Kuong, 2013). In the TNBC samples studiedherein, APOBEC3G expression is significantly correlated with favourableprognosis (log-rank p=0.02) but not other APOBEC members includingAPOBEC3B (p=0.29). APOBEC3G is primarily expressed in CD4+T lymphocytes,macrophages, and dendritic cells (Monajemi, 2012). The present datarevealed strong association between APOBEC3G and natural killer cellgene NKG7 and interleukins in this module and support the importance ofAPOBEC3G in TNBC.

The associations between ITLR and immune-relevant genes, pathways andmodules support the validity of ITLR as a measure of lymphocyticinfiltration and reveal co-regulations of key immune genes.

An aspect of the present invention provides a method of determining aprognosis in a triple negative breast cancer patient, the methodcomprising, determining the level of expression of APOBEC3G in a tumoursample obtained from the patient, wherein increased expression and/orAPOBEC3G expression indicates a positive prognosis.

An aspect of the present invention provides a method of determining aprognosis in a triple negative breast cancer patient, the methodcomprising, determining the level of expression of CTLA4 in a tumoursample obtained from the patient, wherein increased CTLA4 expressionindicates a positive prognosis. Increased CTLA4 expression may be CTLA4expression above the middle (50) percentile for TNBC, or may be CLTA4expression above the (25) percentile for TNBC. Increased CTLA4expression may be CTLA4 expression that is high relative to one or more“housekeeping” genes such as glyceraldehyde-3-phosphate dehydrogenase(GAPDH).

A method of determining a prognosis based on ITLR as described hereinmay further comprise a step of measuring CTLA4 expression in a tumourobtained from the cancer patient. The cancer patient may be a TNBCpatient. The step of measuring CTLA4 expression may involve nucleic acidhybridisation (e.g. microarray-based analysis) or immunohistochemicaltechniques. In such methods, the combination of an ITLR above apredetermined cut-off value and increased CTLA4 expression indicates apositive prognosis. The predetermined cut-off value for ITLR may beabout 0.03 or about about 0.032.

ITLR is an objective quantitative indicator of lymphocytic infiltrationin tumours. This quantitative measurement of immune infiltration isuseful in guiding treatment decisions in cancer.

Accordingly, an aspect of the invention provides a method of using ITLRin predicting whether or not a cancer patient will respond to a therapy.

Such a method may be a method for predicting whether a cancer patientwill respond to a therapeutic regime, the method comprising measuringimmune infiltration in a tumour from the cancer patient according to amethod described herein, wherein an ITLR above a predetermined ITLRcut-off value indicates that the patient is likely to respond to thetherapeutic regime.

ITLR is useful in informing treatment decisions for cancer patients.Accordingly, an aspect of the present invention provides a method oftreating a cancer patient, wherein the ITLR of the tumour has beendetermined to be either below, or above, a predetermined cut-off value.The cancer patient may be an individual from whom an image of a tumourhas been obtained. The method may comprise determining the ITLR in atumour from the patient. The method of treatment may compriseadministration of a therapeutic regime.

The therapeutic regime may be radiotherapy or chemotherapy or anycombination of these. A therapeutic regime comprising chemotherapy maycomprise anthracyline-based chemotherapy. A therapeutic regime maycomprise administration of a therapeutic agent. Accordingly, an aspectof the present invention provides a therapeutic agent for use intreating cancer in a cancer patient, wherein a prognosis for the cancerpatient has been obtained using a method as disclosed herein.

ITLR provides information for predicting a long-term prognosis and forinforming patient treatment decisions. Thus if a patient has a low ITLRand is likely to have a poor prognosis, this patient may be treated moreintensively (e.g. more rounds of chemotherapy) than a patient having ahigh ITLR.

Accordingly, an aspect of the present invention provides a method oftreating cancer in a cancer patient according to a therapeutic regime,the method comprising analysing a tumour image from the cancer patientaccording to a method described herein, and treating the cancer patientaccording to the therapeutic regime depending on whether the ITLR isbelow or above a predetermined cut-off value.

ITLR combined with CTLA4 expression provides further prognosticinformation. Relatively high CTLA4 expression may be associated with ahigh ITLR, and inhibition of CTLA4 may activate T cells to kill cancercells. Thus, for a patient having an ITLR above a predetermined cut-offvalue and having increased CTLA4 expression the therapeutic regime maycomprise administration of a CTLA4 antagonist. The CTLA4 antagonist maybe an antibody, for example ipilimumab.

An aspect of the present invention provides a CTLA4 antagonist for usein a method of treatment of cancer, wherein a tumour from the patienthas been determined to have a high ITLR. An aspect of the presentinvention provides a CTLA4 antagonist for use in a method of treatmentof cancer, wherein a tumour from the patient has been determined to havean ITLR above a predetermined ITLR cut-off value. The cancer may be aspecific type or subtype of cancer and the predetermined ITLR cut-offvalue may be the cut-off value determined for a cohort of patientshaving that cancer type or subtype. The cancer subtype may be breastcancer. The CTLA4 antagonist may be an antibody, which may be ananti-CTLA4 antibody. The anti-CTLA4 antibody may be ipilimumab (alsoknown as MDX-010 and MDX-101). The cancer patient may be a TNBC patientand the therapy may be ipilimumab.

The prognostic and therapeutic methods described herein may furthercomprise surgically resecting a tumour from a cancer patient, measuringimmune infiltration in the tumour according to a method describedherein, and determining a prognosis and/or treating the cancer patientaccording to a therapeutic regime based on the ITLR of the tumour. Asurgically resected tumour is a surgically removed tumour. The method ofmeasuring immune infiltration may use a whole tumour section.

An aspect of the present invention provides a method of determining theefficacy of a therapeutic regime. The method may comprise determiningthe ITLR of a tumour biopsy obtained from a patient before undergoingthe therapeutic regime, determining the ITLR of a tumour biopsy obtainedfrom the patient after undergoing the therapeutic agent, and associatingan increased ITLR with therapeutic efficacy (i.e. a therapeutic effect).

The methods of analyzing tumours according to the invention may bemodified to yield further information on lymphocyte subtypes and theirrelevance in cancer. Lymphocytes in tumours are known to encompassdiverse subclasses including helper T cells, regulatory T cells, naturalkiller cells and B cells with sophisticated implications for treatmentresponse (Fridman, 2012; Gu-Trantien, 2013; Andre, 2013).Immunohistochemistry analysis of tumour sections with immune cellmarkers may be performed, for which automated immunohistochemistry imageanalysis and statistical modelling methods could be developed to discerninteractions between cancer and anti-/pro-tumoural immune response.

In the context of the methods and therapeutic agents described herein, apathological section may be a tumour section. A tumour section may be awhole-tumour section. A whole-tumour section is typically a section cutfrom a surgically resected tumour, thus representing the characteristicsof the whole tumour. Thus, a whole-tumour section may be a surgicallyresected section. A pathological section may be a biopsy obtained from atumour. The pathological section is preferably stained. Stainingfacilitates morphological analysis of tumour sections by colouringcells, subcellular structures and organelles. Any type of staining maybe used, provided that the staining facilitates morphological analysis.The pathological section may be stained with hematoxylin and eosin(H&E). H&E stain is the most commonly used stain in histopathology formedical diagnosis, particularly for the analysis of biopsy sections ofsuspected cancers by pathologists. Thus H&E stained pathologicalsections are usually readily available as part of large data setscollated for the study of cancer. The applicability of the presentmethods to H&E stained pathological sections makes them particularlyadaptable for use in analysing data sets from many types and subtypes ofcancer to determine the prognostic value of ITLR and to determinecut-off ITLR values for use in the methods described herein.

Reference herein to the ITLR of a tumour also refers to the ITLR of apathological section, tumour section, or tumour image.

Reference herein to an ITLR value being below a cut-off value may alsorefer to an ITLR value being equal to or below a cut-off value.

In the present context the term tumour image refers to an image of atumour from a patient. A tumour image may be an image of a pathologicalsection or tumour section. In the present disclosure a patient may bereferred to as having an ITLR (e.g. an ITLR below a predeterminedcut-off value), meaning that an image of a tumour from that patient hasbeen determined to have an ITLR. The tumour image may be of a section ofa surgically resected tumour, or may be of a biopsy of a tumour.

In the present context the ratio of intra-tumour lymphocytes to cancercells (ITLR) is the ratio in the pathological section, in a tumour, in abiopsy from the tumour, in a tumour section, or in an image of thetumour, tumour section or pathological section. The term ITLR may alsobe attributed to a patient. A patient having an ITLR of a particularvalue refers to a patient from whom a pathological section has an ITLRof a particular value.

The terms “lymphocytic infiltration” and “immune infiltration” are usedinterchangeably herein.

In the present context “automated” refers to processes that operateindependent of external (human) control or input. In the present contextan automated process may be a computer-implemented process. The methodsof the present invention may be automated methods. The methods of thepresent invention may be entirely automated methods, that is, they mayoperate independently of human control or input in their entirety. Themethods of the present invention may comprise a step of identifyinglymphocyte and cancer cells by automated image analysis. The methods ofthe present invention may be performed on a tumour image in whichlymphocytes and cancer cells have been identified by automated imageanalysis.

The methods of the present invention are performed on pathologicalsections, such as tumour sections. The methods of the present inventionare therefore ex vivo methods, that is, the methods of the presentinvention are not practiced on the human body.

A cancer patient in the context of the present invention is anindividual having cancer or having been diagnosed with cancer. Referenceto cancer may be reference to a particular type or subtype of cancer.The cancer patient may have undergone anthracyline-based chemotherapy,immunotherapy, or a combination therapy comprising anthracyline-basedchemotherapy and immunotherapy. The cancer patient may have breastcancer, colorectal cancer, melanoma or non-small cell lung cancer. Thecancer patient may have the subtype of breast cancer known as triplenegative breast cancer. Triple negative breast cancer may be defined asa breast cancer that is negative for estrogen receptors (ER) and HER2.(TNBC is sometimes defined as breast cancer that is negative forestrogen receptors (ER), HER2 and progesterone receptors (PR), but sincecancer that are negative for ER are typically also negative for PR, inthe present context TNBC is defined as breast cancer that is negativefor ER and HER2.

In the context of the present invention reference to the treatment of acancer patient refers to treatment of cancer in a patient.

The cancer patient may have, or the cancer type or subtype may beselected from, breast cancer (including breast ductal carcinoma breastand breast lobular carcinoma), central nervous system cancer (includingglioblastoma multiforme and lower grade glioma), endocrine cancer(including adrenocortical carcinoma, papillary thyroid carcinoma,paraganglioma & pheochromocytoma), gastrointestinal cancer (includingCholangiocarcinoma, Colorectal Adenocarcinoma, Liver HepatocellularCarcinoma, Pancreatic Ductal Adenocarcinoma, Stomach-Esophageal Cancer),gynecologic cancer (including Cervical Cancer, Ovarian SerousCystadenocarcinoma, Uterine Carcinosarcoma, Uterine Corpus EndometrialCarcinoma), head and neck cancer (including Head and Neck Squamous CellCarcinoma, Uveal Melanoma), hematologic cancer (including Acute MyeloidLeukemia, and Acute Myeloid Leukemia), skin cancer (including CutaneousMelanoma), soft tissue cancer (including Sarcoma), thoracic cancer(including Lung Adenocarcinoma, Lung Squamous Cell Carcinoma,Mesothelioma) and urologic cancer (including Chromophobe Renal CellCarcinoma, Clear Cell Kidney Carcinoma, Papillary Kidney Carcinoma,Prostate Adenocarcinoma, Testicular Germ Cell Cancer, Urothelial BladderCarcinoma). Each of these cancers is the subject of study as part of TheCancer Genome Atlas project.

In the present context the term “immune signatures” is used to encompassall biomarkers related to immune responses and includes the geneexpression signatures studied herein as well as other biomarkersincluding the “Lym” biomarker (Yuan et al. 2012) and ITLR.

Each and every compatible combination of the embodiments described aboveis explicitly disclosed herein, as if each and every combination wasindividually and explicitly recited.

Various further aspects and embodiments of the present invention will beapparent to those skilled in the art in view of the present disclosure.

“and/or” where used herein is to be taken as specific disclosure of eachof the two specified features or components with or without the other.For example “A and/or B” is to be taken as specific disclosure of eachof (i) A, (ii) B and (iii) A and B, just as if each is set outindividually herein.

Unless context dictates otherwise, the descriptions and definitions ofthe features set out above are not limited to any particular aspect orembodiment of the invention and apply equally to all aspects andembodiments which are described.

Experimental

Methods

Breast Cancer Studies

Clinical Samples

The complete set of METABRIC (Curtis et al.) samples contains 1,980primary frozen breast tumours from five contributing hospitals. Amongthese, 1,026 of the 1,047 tumours from three hospitals have H&E sectionswithout severe artefacts, whist all the H&E samples from the other twohospitals are highly fragmented due to long-term frozen storage.Therefore we only considered the 1,026 tumours for this study (long-termfollow up median 68.3 months). On average three tumour sections wereobtained at different locations of each primary tumour and placed ontothe same slide (Yuan et al., 2012). Tumour materials sandwiched betweenthese sections were sectioned, mixed and used for molecular profiling,thereby maximising the biological relevance of multiple data types beinggenerated. Further details on experimental procedure, staining andmolecular profiling protocols can be found in Yuan et al 2012. Geneexpression data for the same set of tumours were profiled using theIllumina HT-12 platform. ER status was determined based on the bimodaldistribution of ESR1 expression microarray data, and Her2 amplificationstatus based on microarray SNP6 data from the same tumours. In total,there were 181 ER-negative, Her2-negative samples and these were definedas triple negative/TNBC. Samples from two of the three hospitals weremerged to form Cohort 1 (89 samples) and samples from the other hospitalwere merged to form Cohort 2 (92 samples) in order to obtain a similarpopulation size in each cohort. Immune infiltration was scored for 112of the 181 samples by the pathologists in the METABRIC consortium intothree categories: absent, mild and severe. Absent if there were nolymphocytes, mild if there was a light scattering of lymphocytes, andsevere if there was a prominent lymphocytic infiltrate. The pathologicalscores of immune infiltration were not significantly correlated withprognosis (FIG. 13).

H&E Image Analysis

The accuracy of the automated image analysis tool for H&E breast tumourfrozen section images had previously been validated based onpathological tumour scores and cell-by-cell evaluation (Yuan et al.,Natrajan et al.). For METABRIC samples, this tool achieved 90%cross-validation accuracy for cell classification and high correlationwith pathological scores of cell proportions (cor=0.98) (Yuan et al.).This tool was used to classify all cell nuclei in 181 TNBC whole-tumoursections, resulting in an average of 81,810 (standard deviation 80,330)cancer cells, 15,500 (25,133) lymphocytes, and 14,090 (14,180) stromalcells for each image. Lymphocytes have a typical morphology of small,round and homogeneously basophilic nuclei, thus can be reliablydifferentiated from other cell types in cancer. Since this analysis isbased on nuclear morphology only in the H&Es, the identified lymphocytesare likely to be a mixture of immune cell types including T- andB-lymphocytes.

Modelling the Spatial Heterogeneity of Cancer-Immune Interaction

Let x=x₁, x₂, . . . x_(n) be the spatial locations of n cancer cells andy=y₁, y₂, . . . , y_(rn) be the spatial locations of m immune cells in atumour image (e.g. an H&E tumour section image). Using a quartic kernelfunction K one can establish a kernel density estimate over the wholetumour image:

${{f(x)} = \frac{\sum\limits_{i}^{n}{K\left( {x - x_{i}} \right)}}{h}},$

where h is the bandwidth parameter for K. h was optimised using theMinimum Square Error criteria (Berman et al.) in 10 randomly sampledimages. Thus, the spatial proximity to cancer for an immune cell i iss_(i)=f(y_(i)). We can then identify lymphocyte classes based on s,s=s₁, s₂, . . . , s_(m), using unsupervised Gaussian Mixture Clustering(McLachlan, 2000). This method aims to identify multiplecomponents/clusters within the data with probabilities that quantify theuncertainty of observations belonging to the clusters.

${{p(s)}{\sum\limits_{k = 1}^{K}{w_{k}{G\left( {\left. s \middle| \mu_{k} \right.,\sigma_{k}} \right)}}}},$

where K is the number of clusters, μ_(k) and σ_(k) are the mean andvariance that define the probabilistic density function G for the kthcomponent, and w_(k) is the weight of a component k. These parameterswere estimated by Expectation-Maximization (Dempster, 1977). Selectionof models with different numbers of clusters can be done usingstatistical criteria, one of the most common being the BayesianInformation Criterion (Schwartz, 1978). It can be used in conjunctionwith mixture model clustering to select the best number of clusters K:

BIC=2L(p(s))+d log(m)

where L( ) is the maximum log likelihood function and d is the number offree parameters to be estimated. Effectively, the BIC criterion aims toevaluate modelling error as well as model complexity. The higher thevalue of BIC and better the solution is considered to be. To performclustering, 100,000 immune cells were randomly sampled. Their spatialproximity to cancer data s were used for clustering with a range ofdifferent K, K=1-5. This was repeated 200 times, 97% of which thesolution with three clusters was considered the optimal by BIC. Meanμ_(k) of the clusters are consistent (median: 0.011, 0.06, 0.13;standard deviation/SD:

0.002, 0.0047, 0.0045). Subsequently, we classified all lymphocytes inall tumour samples based on these clusters. We used the ratio of thenumber of intra-tumour lymphocytes and the number of cancer cell as thefinal measurement of intra-tumour immune infiltration:

${ITLR} = \frac{N_{{Intra} - {{Tumour}\mspace{14mu} {Lymphocyte}}}}{N_{cancer}}$

Image Analysis and Modelling Spatial Heterogeneity in More Detail

Image Data

CRImage processes a H&E slide by first dividing it into 2,000 pixels by2,000 pixels sub-images and identifying cells in these sub-images.Therefore the cell locations for these sub-images need to be combined.We provide combined cell identifies and spatial locations for all 181TNBC whole-section H&E sections as R data files in a ‘CellPosAndMask’folder. These files are named by their image ID. Each file contain thex, y and class columns storing x y coordinates as well as the class ofeach cell in the large H&E slide. There is also a ‘mask’ binary matrixto denote the tissue area. The resolution of this image is 5 μm perpixel.

Identify the Optimal Bandwidth Parameter for Computing Kernel Density

By sampling 10 random samples, the Mean Square Error is computed over arange of different bandwidths h for computing cancer density.

library(splancs) MSE <− NULL set.seed(10) ffs <−sample(dir(‘./data/CellPosAndMask/’), 10) for (ff in ffs){ res <−try(load(paste(‘./data/CellPosAndMask/’, ff, sep=‘’))) CellPos[,1] <−as.character(CellPos[,1]) CellPos[,2] <− as.numeric(CellPos[,2])CellPos[,3] <− as.numeric(CellPos[,3]) CellPos <−CellPos[rowSums(is.na(CellPos))==0, ] CellPos[,3] <− ncol(Mask) -CellPos[,3] +1 CellPos[,3][ CellPos[,3] > ncol(Mask)] <− ncol(Mask)CellPos <− CellPos[CellPos[,1]!=‘a’,] cell.c <−data.frame(x=as.numeric(CellPos[CellPos[,1]==‘c’,2]),y=as.numeric(CellPos[CellPos[,1]==‘c’,3])) cv <−mse2d(as.points(cell.c), poly=cbind(c(0, 0, nrow(Mask), nrow(Mask)),c(0, ncol(Mask), ncol(Mask), 0)), nsmse=40, range=10) MSE <− rbind(MSE,cv$mse) } save(cv, MSE, file=‘./data/BandwidthSelection.rdata’) h=5 waschosen as the optimal bandwidth for lower variability of Mean SquareError.

Generate Spatial Proximity to Cancer for Each Lymphocyte

Now, spatial scores can be generated given the cell position data usingthe following getITL function. getITL function uses the cell positionfiles to infer a cancer density map using the bandwidth selected above.

getITL <− function(ff, ...){ require(EBImage) require(splancs) res <−try(load(paste(‘./data/CellPosAndMask/’, ff, ‘.rdata’, sep=″))) if(class(res)!=‘try-error’){ CellPos[,1] <− as.character(CellPos[,1])CellPos[,2] <− as.numeric(CellPos[,2]) CellPos[,3] <−as.numeric(CellPos[,3]) CellPos <− CellPos[rowSums(is.na(CellPos))==0, ]CellPos[,3] <− ncol(Mask) - CellPos[,3] +1 CellPos[,3][ CellPos[,3] >ncol(Mask)] <− ncol(Mask) cell.c <−data.frame(x=as.numeric(CellPos[CellPos[,1]==‘c’,2]),y=as.numeric(CellPos[CellPos[,1]==‘c’,3])) res <−kernel2d(as.points(cell.c), poly=cbind(c(0, 0, nrow(Mask), nrow(Mask)),c(0, ncol(Mask), ncol(Mask), 0)), h0=h, nx=dim(Mask)[1],ny=dim(Mask)[2]) cell.l <−data.frame(x=as.numeric(CellPos[CellPos[,1]==‘l’,2]),y=as.numeric(CellPos[CellPos[,1]==‘l’, 3])) z.l <−unlist(sapply(1:length(cell.l$x), function(x) res$z[cell.l$x[x],cell.l$y[x]])) } z.l }

Using this function, measurements for each lymphocyte for each tumourcan then be generated.

itl <− list( ) files <− trait$file for (ff in files) itl <− c(itl,list(try(getITL(ff, h=5, w=3, cex=.5, ifPlot=F)))) names(itl) <− filessave(itl, file=‘./data/ITL.rdata’)

By default getITL function uses the cut-offs (threshold values) of0.10507473 and 0.03662728 to determine intra-tumour (ITL), adjacent totumour (ATL), and distal-to-tumour lymphocytes (DTL). We will nowdescribe how these cut-offs were selected.

Identify Sub-Populations of Lymphocyte by Unsupervised Learning

Gaussian mixture clustering and BIC implemented in the R package mclustwere used for the discovery of lymphocyte sub-populations. 100,000lymphocytes were randomly sampled from the itl object and thenclustered.

library(mclust) load(file=‘./data/ITL.rdata’) set.seed(11) x <−sample(as.numeric(unlist(itl)), 100000) res <− Mclust(x, G=1:5)

The sampling process was repeated to generate clusters 200 times, andevaluated output from Mclust and mclustBIC. BIC values for these 200runs are obtained. The three-cluster solution k=3 remains optimal in 97%of the time, and k=5 was chosen 3% of the times. The median of clustermeans when there are three clusters are 0.0114, 0.0603 and 0.1322 withstandard deviation 0.002 and 0.0047 and 0.0045, respectively.

Therefore, the clustering result of lymphocytes from randomly sampleddata is stable. Since the clustering is stable, cut-offs were taken atthe maximum value of the first and second clusters from one of thesampling runs as our cut-offs for determining lymphocyte classificationfor the remaining samples.

Generating ITLR, ATLR, and DTLR

Subsequently, the cut-offs can be used to classify every lymphocytebased on their data stored in the R object itl. mat.I is a matrix withcolumns of ‘Distal’, ‘Adjacent’, ‘Intra’ denoting the number oflymphocytes in each class for a tumour.

th=c(0.03662728, 0.10507473) mat.l <− NULL for (i in 1:length(itl)){ z.l<− itl[[i]] cl <− rep(1,length(z.l)) cl[z.l>th[1] & z.l<th[2]] <− 2cl[z.l>=th[2]] <− 3 mat.l <− rbind(mat.l, c(sum(cl==1), sum(cl==2),sum(cl==3))) } colnames(mat.l) <− c(‘Distal’, ‘Adjacent’, ‘Intra’)rownames(mat.l) <− names(itl)

The Intra column of mat.I is the number of intra-tumour lymphocytes.This divided by the number of cancer cells (trait$nTumour) is the ITLRmeasurement

Measuring Cell Distances and Spatial Arrangement

To identify physical properties of ITLs, ATLs and DTLs thatdifferentiate them, in 10,000 lymphocytes randomly sampled from 20tumours, we identified the 5 nearest cancer cells and the centroid ofthe convex hull region formed by these cancer cells. For eachlymphocyte, the distance from the lymphocyte to the nearest cancer cellwas computed (d_(min)), and the distance to the centroid of cancerconvex hull was computed (d_(centroid)). Centroid of a convex hullregion was calculated as the mean positions of the subset of points thatdefine the convex hull. Differences among lymphocyte classes in terms ofd_(centroid and) and d_(centroid) were tested with student's t-test.

Other Immune Signatures in Comparison

Lymphocyte abundance based on image analysis result was calculated as:

${lym} = \frac{N_{lymphocyte}}{N_{cancer}}$

The gene expression signatures were calculated as described in thereferred papers.

ITLR Gene Modules

Hierarchical clustering was used to identify highly correlated genemodules by clustering the correlation matrix of all ITL-associated genesinto 100 clusters. Modules were selected from these clusters based onaverage absolute Pearson correlation exceeding 0.75 and cluster sizeexceeding five.

Comparing ITLR and ITLR-Associated Genes

To test if ITLR has additional value to ITLR-associated genes, weperformed multivariate Cox regression analysis with ITLR paired withexpression profile of an ITLR gene. This was performed for all of thetop 100 ITLR-associated genes ranked by correlation. ITLR wasdichotomised using the threshold reported in the paper, and geneexpression was dichotomised into two equal-size group or three groups(25 lower, 50 middle and 25 upper percentiles). Tables with Hazardratio, log-rank p-value and 95% interval were produced. In both analysiswith two and three patient groups according to gene expression data,p-values of ITLR were consistently higher than the p-values of geneexpression profiles, as well as being higher than significance level of0.05 (−log(p) 2.99).

Other Statistical Methods

Monotone trend between ITLR and clinical parameters was tested using theJonckheere-Terpstra trend test (Jonckheere). Survival analysis wasperformed with breast cancer-specific 10-year survival data. TheKaplan-Meier estimator was used for patient stratification and log-ranktest was used for testing difference among groups. Cox proportionalhazards regression model was fitted to the survival data and hazardratios and 95% confidence intervals were computed to determine thecorrelation with disease-specific survival, where the log-rank test withp<0.05 was considered significant. Correlation between ITLR and geneexpression was computed with Pearson correlation and q-values computedusing False Discovery Rate (FDR) correction using 25% of the data forfitting the null model. Cut-offs for dichotomizing immune signatureswere optimised stepwise from 20 to 80 percentiles at an interval of 1.5.The cut-offs that displayed the highest prognostic significance withlog-rank test were selected. For consistency test in FIG. 5F, eachsignature was centred at 0 and scaled to standard deviation 1. Optimalcut-offs were also mapped to the new data before comparison. MSigDB geneset version 4.0 (Subramanian et al.) was used in conjunction with ahypergeometric test for enrichment analysis.

Ovarian Cancer Studies

Samples were obtained from a UK-China collaborative study which aims tostudy the clinical implications of immune infiltration in a set of 91ovarian cancer patients with metastatic disease. H&E-stained slides forthe primary tumours were obtained, scanned, and subjected to imageanalysis using CRImage. Cells in these images were classified intocancer, lymphocyte, and stromal cell categories. Once the spatiallocations of these cells were obtained from image analysis, kerneldensity of cancer was computed for each image, and lymphocyte-to-cancermeasurements were obtained for each lymphocyte. The measurements weresubjected to clustering and two clusters were found, i.e. intra-tumourand non-intra tumour lymphocytes. ITLR as the ratio of intra-tumourlymphocytes to cancer cells was calculated for each patient. The 29% ofpatients with ITLR lower than a cut-off of 0.06085726 have significantlyworst overall survival than patients with ITLR higher than the cut-off(10-year OS log-rank test p=0.024, HR=0.51, CI=0.28-0.92; 5-year OSp=0.045, HR=0.54, CI=0.29-0.99; Figs Ovarian). Overall survival wasdefined using death as event regardless of the cause, as thisinformation was unavailable.

Tumours were staged according to the 1988 FIGO staging system (Prat2013). Lymphocytic infiltration was assessed in five high-power fields,each field is scored as absent, mild, or severe: Absent if there were nolymphocytes, mild if there was a light scattering of lymphocytes, andsevere if there was a prominent lymphocytic infiltrate. Median offield-based scores was taken as the score for a tumour.

Results

Statistical Modelling of the Spatial Heterogeneity of ImmuneInfiltration—Determination of ITLR

An automated image analysis tool identified cancer, lymphocytes andstromal cells encompassing fibroblasts and endothelial cells based ontheir nuclear morphologies in H&E whole-tumour section slides (Yuan etal. 2012). The main component of this tool is a classifier trained bypathologists over randomly selected tumour regions and validated in 564breast tumours with 90% accuracy (Yuan et al. 2012). As a result ofimage analysis, the types and spatial locations of on average 110,000cells were recorded in every breast tumour section. Thus, this fullyautomated tool enabled the mapping of spatial distributions of allcancer cells and lymphocytes within a tumour section, which can besubsequently visualised as a 3D landscape (FIG. 1A). The spatialrelationships of immune and cancer cells are then analysed with astatistical pipeline exemplified in FIG. 1B. First, to globally profilethe spatial distribution of the cancer cells, the cancer cell densitywas quantified using a kernel estimate (Methods). Intuitively, thisbuilds a ‘cancer landscape’ where hills indicate tumour regions denselypopulated with cancer cells. The height of a hill thus correlates withcancer density at a specific location in the tumour (FIG. 1B). Secondly,for every lymphocyte, its spatial proximity to cancer can be directlyquantified with the cancer density landscape at its specific location.Thus a quantitative measurement of the spatial proximity to tumour cellscan be efficiently obtained for every lymphocyte (FIG. 1B).

Using this approach, we quantified the spatial proximity to cancer forevery lymphocyte in 181 TNBC samples in the METABRIC study (Methods,FIG. 2A). In principle, lymphocytes that differ in their spatialpositioning to cancer can be differentiated based on these quantitativespatial measurements. The inventor investigated whether data-drivenclustering methods based on normal distribution can be used todifferentiate different classes of lymphocytes, since cell spatialdistribution is a naturally emerged pattern. Unsupervised GaussianMixture Model clustering Fraley, 2003) was employed to identifylymphocyte clusters based on their spatial proximity to cancer using atraining set of 100,000 randomly sampled lymphocytes (FIG. 2B, Methods).Subsequently, a three-cluster solution that identify three classes oflymphocytes was considered the optimal by the Bayesian InformationCriterion (Schwartz, 1978) (FIG. 2B). This three-class solution isconsistently the optimal 97% of the time upon 200 repeated sampling,whilst the five-class solution was considered optimal 3% of the time(Methods, FIG. 2C). In addition, the cluster structure of thethree-class solutions was stable (median of cluster mean: 0.011, 0.06,0.13; standard deviation/SD: 0.002, 0.0047, 0.0045; FIG. 2C), indicatingthat the same clusters were identified in each random sampling. Thethree classes of lymphocytes were named as Intra-Tumour Lymphocyte(ITL), Adjacent-Tumour Lymphocyte (ATL) and Distal-Tumour Lymphocyte(DTL). Subsequently, a classifier was trained based on the lymphocyteclasses to predict the types of lymphocytes in all TNBC samples(Methods).

To understand the differences of the newly proposed lymphocyte classes,additional measures were derived that are based on direct physicaldistances. First, for each lymphocyte its distance to the nearest cancercell can be quantified (d_(min), Methods, FIG. 2D). It was shown thatITLs have a median distance of 7 μm (interquartile range 5-10) to thenearest cancer cell, whilst it is 10 μm (7-11) for ATLs, and 20 μm(14-26) for DTLs (FIG. 2E). The overlap in distance to nearest cancercell between ITLs and ATLs suggests that this measure is not thefundamental difference between the two classes. Since the kernel densitymeasure based on which the lymphocyte classes were derived isessentially spatial smoothing, the inventor hypothesised that thespatial arrangement of cancer cells surrounding lymphocytes differsbetween ATLs and ITLs. To measure spatial arrangement, the inventorexamined the convex hull region formed by 5 nearest cancer cells, whichis the smallest region that covers these cells (FIG. 2D, Methods). If alymphocyte is surrounded by cancer cells, it should fall into the convexhull region formed by nearby cancer cells and has a small distance tothe centroid of this region (FIG. 2D, left). In contrast, if nearbycancer cells are to one side of a lymphocyte, the distance between thelymphocyte and the centroid of the cancer convex hull region is likelyto be large (FIG. 2D, right). Thus, the inventor used the distancebetween a lymphocyte and the centroid of the cancer convex hull regionas a quantitative measure of the spatial arrangement of cancer cellssurrounding a lymphocyte (d_(centroid)) Three lymphocyte classesdisplayed significant differences in d_(centroid) with mediand_(centroid) 3.6 μm (2.2-5.1), 7.2 μm (4.5-10.6), 17.7 μm (11.0-26.6)for ITLs, ATLs, and DTLs, respectively (FIG. 2E). Therefore, d_(mind)and d_(centroid) together better define and aid interpretation of thelymphocyte classes (FIG. 2F). Taken together, these data demonstratedthat the proposed kernel-based measure of spatial proximity to cancercan effectively account for spatial proximity and surroundings, and alsothat the three lymphocyte classes differ not only in the distance to thenearest cancer cell but also in the ways nearby cancer cells arearranged. A representative case showing spatial distribution oflymphocytes in these three classes is illustrated (FIG. 3A-B). Forinstance, the ITLs can be observed to locate within regions denselypopulated with cancer cells (FIG. 3C).

In the 181 TNBC samples, there are overall more ATLs than the other twotypes of lymphocytes (on average 47% ATLs, 32% ITLs and 21% DTLs, FIG.4A). The changes in abundance of these three classes in 181 samples canbe observed in a triangle plot (FIG. 4B). When the proportion of ITLs islow (0-20%), there are in general more DTLs (40-60%) than ATLs (30-50%).As the amount the ITLs increase (20-50%), ATLs also increase (40-60%)while DTLs decrease (10-40%). When there are large amount of ITLs(>50%), there are still certain amount of ATLs (20-40%) with very fewDTLs (<10%). To summarise the degree of lymphocytic infiltration for agiven tumour, we first calculated the ratio between the number of ITLsand the number of cancer cells (ITLR; see Methods above). In the 181TNBC samples, a significant association was observed between ITLR andpathological assessment of lymphocytic infiltration of the tumours incategories of absent, mild and severe (p=2×10⁻³³, FIG. 4C). In terms ofother clinical parameters, there was no correlation between ITLR andtumour size, node status and TP53 mutation status (FIG. 4D). Tumourgrade was not considered because 87% of the TNBC samples are Grade 3tumours. These data support ITLR's validity as a measurement oflymphocytic infiltration and its potential value in addition to knownclinical parameters for TNBC.

ITLR is a Statistical Measure of Lymphocytic Infiltration and anIndependent Predictor of Disease-Specific Survival in Two TNBC Cohorts.

To investigate the clinical significance of the proposed immune measureITL, the inventor analysed disease-specific survival as a function ofITL. The TNBC samples can be divided into two independent cohorts basedon contributing hospitals (Methods, n=89 and n=92, distribution of ITLRFIG. 3E). To dichotomise the continuous ITLR, the optimal cut-off wasselected to have the best prognostic value in Cohort 1 as the discoverycohort (Methods). The best cut-off was selected to be 0.011 and 20% ofthe patients have ITLR lower than this cut-off. These patients havesignificantly worse disease-specific survival compared with patientswith higher ITLR in Cohort 1 (Log-rank test p=0.0063, Hazard ratioHR=0.36, 95% confidence interval CI=0.17-0.77; Table 1; FIG. 4F). Thisobservation was verified in the validation cohort, Cohort 2 (p=0.0037,HR=0.25, CI=0.09-0.69; FIG. 4F). Significant stratification was observedupon repeated analysis with Cohort 2 as the discovery and Cohort 1 asthe validation cohort (FIG. 3G). The same tests were performed for theratio of ATLs and DTLs to cancer cells (ATLR and DTLR), but neithershowed a significant correlation with disease-specific survival(Discovery and Validation cohort: ATLR p=0.064 and 0.75; DTLR p=0.43 and0.25; FIG. 7-8). We subsequently focused on ITLR. ITLR-high TNBCpatients have a survival probability of 80% five year from diagnosisversus 49% for ITLR-low patients (Kaplan-Meier survival estimates, twocohorts combined).

ITLR was compared with eight other immune signatures. These include thepreviously published image-based signature, lymphocyte abundance (Lym),defined as the ratio between the number of lymphocytes and the number ofcancer cells (Methods) (Yuan et al. 2012). A major difference betweenITLR and Lym is that Lym does not account for different classes oflymphocytes whilst ITLR considers infiltrating lymphocytes. Theremainder of signatures are published gene expression-based signaturesfrom Calabro et al. (Calabro et al.) that is predictive of ER-negativebreast cancer prognosis, a 5-gene signature from Ascierto et al.(Ascierto et al. '12) that predicts recurrence-free survival acrossbreast cancer subtypes, and the B-cell, IL8 and combined signatures topredict prognosis of TNBC (Rody et al.). CXCR3 and CXCL13 expressionwere also included since they have been shown to correlate with breastcancer prognosis (Ma et al., Gu-Trantien et al.).

The same cut-off selection approach was applied to test the associationbetween these signatures and disease-specific survival (Table 2). Thesignatures that showed the best prognostic values are shown in FIG. 5A-E(all are provided in FIG. 9) and Table 1. None of these signaturescorrelated with prognosis in both cohorts. This analysis was repeatedusing Cohort 2 as the discovery cohort for selecting the optimalcut-offs and Cohort 1 for validation (FIG. 10, Table 3). In bothexperiments, only ITLR consistently stratified patients into two groupsof different outcome among the nine signatures (FIGS. 7 and 8).Furthermore, the best ITLR cut-offs selected in two cohorts for all ninesignatures were compared (Methods, FIG. 5F). ITLR was among the mostconsistent signatures in terms of optimal cut-offs in two cohorts,supporting the consistency and the potential use of ITLR as an objectivemeasure for identifying patients with low lymphocytic infiltration.

Compared to published immune signatures, ITLR was also the onlysignature to show significant correlation with disease-specific survivalin multivariate Cox proportional hazards model together with standardclinical parameters of nodal status and tumour size in both cohorts,whichever cohort was used as the discovery cohort (Tables 1 to 3). Usingsamples from both cohorts, ITLR has a log-rank p-value of 2.1×10⁻⁴ andHR 0.32 (0.17-0.58). To test the robustness of the Cox model indetermining the prognostic value of ITLR, we used bootstrap analysis inrandomly perturbed data and repeated the univariate and multivariateregression analysis 1,000 times. In 95.6% and 94.7% of the time, ITLRremained significantly associated with prognosis in univariate andmultivariate analysis, respectively. Taken together, these results showthe stability and robustness of ITLR as an independent prognosticbiomarker in TNBC.

ITLR Heterogeneity is Reflected on the Transcriptional Level by CTLA4and APOBEC3G Expression

To identify molecular associations of immune infiltration and to testthe biological relevance of ITLR, the inventor integrated image-basedITLR with microarray gene expression data profiled for the same set of181 TNBC tumours. The analysis identified 307 genes positivelycorrelated and 105 genes negatively correlated with ITLR (FalseDiscovery Rate multiple testing correction, q-value<0.05; Methods).Genes with the most significant correlations with our immune signatureITLR include kinases (SH3KBP1, LCK, MAP4K1) and receptors (FCRL3, GPR18,TNFRSF13B, SEMA4D, CXCR3, IL2RG), as well as the known immunotherapytarget CTLA4 (Table 4). Thus, significant correlations between ITLR andimmune-related genes demonstrate the biological relevance of the ITLRsignature.

TABLE 4 Top 20 genes positively correlated with ITLR and top 10 genesnegatively correlated with ITLR (underline). Symbol Cytoband Descriptioncor q SH3KBP1 Xp22.12b SH3-domain kinase binding protein 1 0.4  0.0011FCRL3 1q23.1d Fc receptor-like 3 0.4  0.0011 LCK 1p35.1blymphocyte-specific protein tyrosine kinase 0.4  0.0011 GPR18 13q32.3a Gprotein-coupled receptor 18 0.39 0.0011 TNFRSF13B 17p11.2h tumournecrosis factor receptor superfamily, 0.39 0.0011 member 13B SEMA4D/9q22.2a sema domain, immunoglobulin domain (Ig), 0.39 0.0012 CD100transmembrane domain (TM) and short cytoplasmic domain, (semaphorin) 4DMAP4K1 19q13.2a mitogen-activated protein kinase kinase kinase 0.390.0012 kinase 1 RLTPR 16q22.1b RGD motif, leucine rich repeats,tropomodulin 0.38 0.0012 domain and proline-rich containing UBASH3A21q22.3b ubiquitin associated and SH3 domain 0.38 0.0012 containing AIKZF3 17q12c IKAROS family zinc finger 3 (Aiolos) 0.38 0.0012 CYFIP25q33.3a- cytoplasmic FMR1 interacting protein 2 0.38 0.0012 q33.3b CXCR3Xq13.1d chemokine (C-X-C motif) receptor 3 0.38 0.0012 CD3E 11q23.3dCD3e molecule, epsilon (CD3-TCR complex) 0.38 0.0012 IL2RG Xq13.1cinterleukin 2 receptor, gamma 0.38 0.0012 CXCR5 11q23.3e chemokine(C-X-C motif) receptor 5 0.38 0.0014 CTSW 11q13.1d cathepsin W 0.370.0018 SH2D1A Xq25c SH2 domain containing 1A 0.37 0.0018 SEPT6 Xq24cseptin 6 0.37 0.0018 CTLA4 2q33.2a cytotoxic T-lymphocyte-associatedprotein 4 0.37 0.0019 SIRPG 20p13e signal-regulatory protein gamma 0.370.0019 C10orf141 10q26.2b −0.4  0.0011 CD151 11p15.5cCD151 molecule (Raph blood group) −0.39  0.0011 SPP1 4q22.1bsecreted phosohoprotein 1 −0.39  0.0012 ANXA2 15q22.2a annexin A2 −0.39 0.0012 P4HA2 5q31.1b prolyl 4-hydroxylase, alpha polypeptide II −0.36 0.0022 MUSK 9q31.3b muscle, skeletal, receptor tyrosine kinase −0.36 0.0023 POFUT2 21q22.3e protein O-fucosyltransferase 2 −0.36  0.0025ITGB5 3q21.2a integrin, beta 5 −0.35  0.004  MXRA7 17q25.1d-matrix-remodelling associated 7 −0.34  0.0046 q25.2a CALN1 7q11.22ccalneuron 1 −0.34  0.0046

Subsequently, enrichment analysis was performed on the positively andnegatively correlated genes respectively against MSigDB gene setcategories (Subramanian, 2005) including KEGG pathways (Kanehisa, 2000),canonical pathways curated by domain experts and immunologic signatures(Methods, FIG. 11). Genes positively correlated with ITLR are enrichedwith natural killer cell mediated cytotoxicity, T cell receptor, Antigenprocessing and presentation KEGG pathways, CD8 T cell, CD4 T cell and Bcell up-regulated immunogenic signatures, as well as IL12 and CD8 TCRcanonical pathways. Conversely, genes negatively correlated with ITLRwere enriched with ECM receptor interaction and focal adhesion KEGGpathways, regulatory T cell and TGFβ related immunologic signatures aswell as integrin related pathways. The molecular analysis on the pathwaylevel suggests ITLR is positively associated with anti-tumour immuneactivities in TNBC.

To further dissect their interconnected relationships and discover denovo molecular modules, tightly connected gene modules were identifiedwithin ITLR-associated genes (FIG. 11; Methods). As such, seven modulesof positively correlated genes (P1-7) and two modules of genesnegatively correlated with ITLR (N1 and N2) were identified. Knownimmune-related genes in the modules include IFNG (P1), RLPTR (P3), GPR18(P4), CXCR3 (P5), MAP4K1 (P6), CTLA4 (P7), ANXA2 (N1) and FAP (N2).Notably, two of the modules contain APOBEC3G (P2) and CTLA4 (P7), whichmay suggest co-regulation among APOBEC3G, NKG7 and interleukinsincluding IL21R and IL18RAP, as well as high correlations among CTLA4,chemoattractant for B lymphocytes CXCL13 (Denkert) and TIGIT T cellimmunoreceptor with Ig and ITIM domains. Furthermore, expressionprofiles of these genes were significantly associated withdisease-specific survival in TNBC, including APOBEC3G as well as GPR18(P4) and MAP4K1 (P6) ranked as the top ITLR-associated genes (FIG. 6B,FIG. 12). CTLA4 expression was able to stratify patients into groupswith significantly different prognosis, and could further stratify theITLR high group into two subgroups with significantly different outcome(p=0.046, FIG. 6C, FIG. 12). Comparing ITLR with ITLR-associated genesin terms of prognostic value, multivariate analysis showed that ITLRstratification has additional and in many cases superior value toITLR-associated genes (FIG. 13, Methods).

REFERENCES

A number of publications are cited above in order to more fully describeand disclose the invention and the state of the art to which theinvention pertains. Full citations for these references are providedbelow. The entirety of each of these references is incorporated herein.

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1. A method of measuring immune infiltration in a tumour, the methodcomprising: providing an image of the tumour in which lymphocytes andcancer cells have been identified; obtaining a lymphocyte-to-cancermeasurement for each lymphocyte; classifying a subset of the lymphocytesas intra-tumour lymphocytes according to their lymphocyte-to-cancermeasurement; quantifying the intra-tumour lymphocytes and the cancercells in the tumour image; calculating the intra-tumour lymphocyte ratio(ITLR) as the ratio of intra-tumour lymphocytes to cancer cells, whereinthe ITLR is a measurement of immune infiltration in the tumour.
 2. Themethod according to claim 1, wherein the step of obtaining alymphocyte-to-cancer measurement for each lymphocyte comprises: applyinga density estimate to obtain a model of the cancer cell density; anddetermining the proximity of each lymphocyte to cancer cell density. 3.The method according to claim 1, wherein a lymphocyte is classified asan intra-tumour lymphocyte if its lymphocyte-to-cancer measurement isabove a threshold value. 4.-5. (canceled)
 6. The method according toclaim 1, further comprising a step of identifying the lymphocytes andcancer cells in an image of the tumour by automated image analysis toprovide an image of the tumour in which lymphocytes and cancer cellshave been identified.
 7. The method according to claim 1 wherein thetumor is a tumor sample from a cancer patient having breast cancer,ovarian cancer, colorectal cancer, melanoma or non-small cell lungcancer.
 8. The method of according to claim 7 wherein the cancer patienthas breast cancer which is triple negative breast cancer. 9.-11.(canceled)
 12. A method of treating cancer in a cancer patient accordingto a therapeutic regime, the method comprising analysing a tumour imagefrom the cancer patient according to the method of claim 1, and treatingthe cancer patient according to the therapeutic regime depending onwhether the ITLR is below or above a predetermined cut-off value. 13.The method according to claim 12, wherein the method further comprisessurgically resecting a tumour from the cancer patient and measuringimmune filtration in the surgically resected tumour.
 14. The method oftreating cancer according to claim 12, wherein the cancer patient hastriple negative breast cancer, wherein the therapeutic regime comprisesadministration of a CTLA4 antagonist, and wherein the cancer patient istreated according to the therapeutic regime if the ITLR is above apredetermined cut-off value.
 15. The method according to claim 12,wherein the image of a tumour is an image of a hematoxylin and eosinstained tumour section. 16.-18. (canceled)